1. Introduction

Compared with conventional power grids, a smart grid (SG) can provide a more intelligent, efficient, reliable, and sustainable power service by utilizing advanced structure and information technology [1]. Therefore, the nature of industrial utility and users will be greedy. Conventional grids cannot satisfy both parties simultaneously to address the latest grid difficulties such as robustness, reliability, and so on. The novel and intellectual idea of the SG renovates conventional grids through the integration of modern communication technology. In this context, consumers are involved in the trade of energy [2]. The bidirectional transmission or energy flow benefits the industrial utility and the users. Notably, consumers do not remain just consumers; they also become “prosumers” who are capable of accessing the electricity market as both buyers and sellers [3]. Concurrently, the smart industrial utility is able to efficiently manage its sources. To maximize SG performance, mainly in terms of dispersal, a decision-making entity is required. Proper decision making results in electricity cost reductions for users, together with the reduction of overall energy losses and mitigation of the peak-to-average ratio [4]. Considering these objectives, recent research in SG has primarily focused on the optimized methods of power scheduling. However, before scheduling, a precise short-term load forecasting (STLF) method is required to properly plan the ongoing grid functioning needed for effective resource management [5]. A high degree of randomness and non-linearity in historical load curves make STLF very challenging. In this study, several STLF techniques were proposed. However, the accuracy of such techniques was either not satisfying or their convergence rates were slow.

Load prediction is a crucial outcome of an SG mechanism. It predicts the long-term, short-term, and medium-term demand for electrical energy from consumers [6]. Deep learning (DL) can be broadly utilized for load prediction in SGs. Though a subset of machine learning (ML), DL is highly useful compared with other conventional ML techniques. Moreover, it eases the utility of other ML techniques combined with ANN for superior outcomes [7]. Several methods are integrated with ANN for smart grids, which include K-nearest Neighbor (KNN), Random Forrest (RF), other ML methodologies, Support Vector Machine (SVM), and Decision Tree (DT), achieving superior outcomes for load forecasting, power management, and other purposes [8]. Power distribution firms especially benefit from SGs through the determination of superior power distribution between the clients or consumers (corporate office, home, and industry) [9]. Presently, diverse electrical power consumption data can be aggregated centrally. DL can perform load prediction from this vast amount of information, therefore enabling power distributors to estimate demand for power distribution [10].

For load forecasting, this study focused on the development of a new wild horse optimization method with a deep learning-based STLF scheme (WHODL-STLFS) for SGs. The presented WHODL-STLFS technique was initially used to design a WHO algorithm for the optimum selection of features from the electricity data. The WHO algorithm mimics horse behavior in which they leave their group and join another group before becoming adults to prevent mating between siblings or fathers and daughters. The WHO algorithm was chosen because it properly balances the exploration and exploitation phases and achieves the optimal global solution. In addition, attention-based long short-term memory (ALSTM) was exploited to learn the energy consumption behaviors in order to forecast load. Finally, an artificial algae optimization (AAO) algorithm was applied as the hyperparameter optimizer of the ALSTM model. The experimental validation of the WHODL-STLFS technology was carried out under several measures. In short, the key contributions of the paper are as follows:

• An intelligent WHODL-STLFS technique comprising WHO-based feature selection, ALSTM-based energy consumption prediction, and AAO-based hyperparameter tuning was presented for STLF in SGs. To the best of our knowledge, the WHODL-STLFS technique is not present in the existing literature.

• A WHO algorithm was designed for the optimal selection of features in the STLF process in the SGs, which helps to reduce the computational complexity.

• An ALSTM model was employed for the load-prediction process in the SG environment.

• An AAO algorithm was presented for the parameter tuning of the ALSTM model, which helps to improve the prediction performance with minimal resources.

• The performance of the WHODL-STLFS technique on an FE grid and a Dayton grid was investigated.

2. Literature Review

In ref. [11], mid-term and STLF (MTLF and STLF) were modeled with the use of smart-metered data obtained from a real-time distribution grid at the NIT Patna campus using various traditional and ML approaches. Data preprocessing can be carried out to transform the raw data into a suitable form through the removal of the outliers presented in the data. Moreover, the influential weather-related parameters acquired by correlation analysis, including the past load, can be utilized for training the load forecasting method. In ref. [12], an accurate and fast hybrid electric load forecasting (FA-HELF) structure was modeled. Firstly, RF and relief-F techniques were compiled together to devise a hybrid feature selection method for eliminating redundancy. Secondly, kernel-related PCA was presented for extracting features to overcome the issue of dimension reduction. Lastly, for execution, an FA-HELF optimizer was compiled with an SVM forecaster.

Syed et al. [13] modeled a new hybrid clustering-related DL technique for STLF at the distribution transformer levels with boosted scalability. The study examined the benefit of training duration and the presentation relating to accuracy when clustering-related DL modeling was used for STLF. A k-Medoid-related technique was leveraged for clustering, whereas predictive techniques were generated for various clusters of load profiles. In ref. [14], a novel hybrid STLF was devised. The data preprocessing and feature-selection elements depend upon the modified mutual information (MMI) method, which is an enhanced version of the mutual information method, utilized for selecting abstractive attributes from historical data. The training and forecasting element depends upon factored conditional restricted Boltzmann machines (FCRBMs), known as a DL technique, empowered through learning to predict the future electric load. The optimized method relies upon this modeled genetic wind-driven (GWDO) optimized technique, which was employed for fine-tuning the adaptable variable of the method.

An enhanced STLF was modeled in [15]. Initially, the load was decomposed into distinct frequency elements, changing from lower to higher levels comprehended by the ensemble empirical-mode decomposition method. Following this, the smooth and periodic lower-frequency element was predicted through a multivariate linear regression model while sustaining computation ability, while the higher-frequency element with a high degree of randomness was predicted by LSTM-NN techniques. In ref. [16], a new DL-related technique was applied for electricity load forecasting. A three-step method was applied with feature selections utilizing a hybrid feature selector (DT and XGboost), a redundancy removal utilizing a feature-extracting method (Recursive Feature Elimination), and forecasting or classification through an enhanced Extreme Learning Machine (ELM) and SVM. Ünal et al. [17] proposed an advanced pre-processing system combined as a hybrid sequential learning-related energy forecasting system, which utilized a CNN and BLSTM in a unified structure for an accurate energy-consumption forecast. The new hybrid DL technique related to data feature decoding and coding was enforced in the forecasting phase.

3. Materials and Methods

In this study, new WHODL-STLFS technology was developed for accurate and timely load forecasting for SGs. The presented WHODL-STLFS technique encompasses a three-stage process. At the initial level, the WHODL-STLFS technique used the WHO-based feature-selection process. Next, the ALSTM model was exploited for predicting load in SGs. Lastly, the AAO algorithm was employed as the hyperparameter optimizer of the ALSTM model. Figure 1 illustrates the working process of the WHODL-STLFS system.

3.1. Feature-Selection Technique

Firstly, the WHODL-STLFS technique used the WHO-based feature selection process. The WHO approach is a metaheuristic technique inspired by the social behavior of wild horses [18]. In the presented method, various efficiencies are established using the behaviors of wild horses, such as mating, leading, chasing, grazing, and hunting. The horses were classified into two social groups, namely non-territorial and territorial. However, the WHO approach focuses on non-territorial groups that encompass group leaders (stallions), various mares, and their offspring. Moreover, if the foals exceed the age of puberty, they leave their group and incorporate into others. The WHO technique was defined in the following phases.

3.1.1. Population Initialization

In this phase, the variable needed for the WHO algorithm is determined to estimate the initial solution:

(1)$H=Q\times SR$

In Equation (1), $H$ indicates the whole quantity of groups, $Q$ represents the population size, and $SR$ denotes the stallion count from the population.

3.1.2. Grazing Behavior

This phase introduces the grazing performance of foals before they reach puberty. The stallion is regarded at the center of the grazing region, while the residual group members neighbor the center of the region as follows:

(2)$X_{i+1,H}^j=2A\times cos\left(2pRA\right)\times \left(S^j-X_{i,H}^j\right)+S^j$

Here, $i$ indicates the group member count, $j$ represents the stallion count, $X_{?+1,H}^j,$ $X_{i,H}^j$ refers to the member position from the subsequent and existing iteration, $A$ indicates an arbitrarily selective adaptive procedure, $R$ signifies the arbitrary value in -2 and 2, and $S^j$ indicates the stallion placement.

3.1.3. Horse Mating Behavior

This stage provides the performance of foals after reaching the age of puberty. As noted above, foals leave the group and integrate with other groups to mate and prevent fathers from marrying their sisters and daughters. Foal performance can be determined as follows:

(3)$X_{H,l}^t=Mean\left(X_{H,i}^u,X_{H,j}^w\right)andi?j?l$

In Equation (3), $X_{H,l}^t$ indicates the location of horse $t$ from group $l,$ $X_{H,i}^u$ represents the position of foal $u$ from group $i$, and $X_{H,j}^w$ denotes the location of foal $w$ from group $j$, whereby foal $u$ mates with foal $w$ from group $l$. Consequently, a state fundamental to mating is attained.

3.1.4. Group Leadership

The group stallion leads the group member to the waterhole for food in this phase. Similarly, the stallion fights with other stallions to dominate the waterhole, which is determined by the following:

(4)$S_{?+1,G}=\left\{\begin{array}{l}2A\times cos\left(2pRA\right)\times \left(WP-S_{i,G}\right)+WPif{r}_1>0.5\hfill \\ 2A\times cos\left(2pRA\right)\times \left(WP-S_{i,G}\right)-WPif{r}_1=0.5\hfill \end{array}\right.$

From the expression, $S_{?+1,G},S_{?,G}$ shows the following and existing position of leader, $WP$ indicates the position of waterhole, and $r_1$ indicates a random number within [0, 1].

3.1.5. Leader Exchange and Selection

Eventually, the group leader was carefully chosen to obtain optimal fitness values. In each iteration, the group leader was carefully chosen, while an optimal leader could be identified among all the leaders from the iteration:

(5)$S_{i,G}=\left\{\begin{array}{l}{X}_{i,G}ifcost\left(X_{i,G}\right)<cost\left(S_{i,G}\right)\hfill \\ S_{i,G}ifcost\left(X_{i,G}\right)>cost\left(S_{i,G}\right)\hfill \end{array}\right.$

In the presented method, the termination criteria were used to implement the enhanced procedure up to the maximum iteration count (Max. It) as explained in Algorithm 1 below.

3.2. Load Forecasting Scheme

At this stage, the ALSTM model was exploited for predicting load in SGs. The RNN is a type of NN, whereas the outcome of a feed-forward typical ANN is offered as a novel input to neurons depending on the new input value [19]. The outcome value at any neuron $\left(z+1\right)$ relies on its input at moment $z$. This enhances the dynamism of the network system. Assuming there is a connection between two input values, this technique is resolved as a memory-network approach. In RNN, input data are supposed to connect to everyone. The LSTM is the best-known RNN network approach, but the infrastructure has been introduced to address the vanishing gradient problem. At present, $w_z$ refers to the input value at time $z$, and $0_z$ signifies the outcome value at time $z$.

The infrastructure of the LSTM network node comprises three basic gates: output gate $0_z$, forget gate $f_z$, and input gate $i_z$. However, the input and output gates represent the data that entered and data that exited the node at time $z$, respectively. The forget gate selective data that were forgotten were connected to previous status data $\left(h_{z-1}\right)$ and current input $\left(x_z\right)$. These three selected gates upgraded the current memory cell $c_z$ and current latency $h_z$ value. In the LSTM node, the links between the gates were calculated mathematically, employing the following equation:

(6)$i_z=s\left(w_i.\left[h_{z-1},x_z\right]+b_i\right)$

(7)$f_z=s\left(w_f.\left[h_{z-1},x_z\right]+b_f\right)$

(8)$o_z=s\left(w_o.\left[h_{z-1},x_z\right]+b_o\right)$

(9)$c_z^{\text{'}}=tanh\left(w_c.\left[h_{z-1},x_z\right]+b_c\right)$

(10)$c_z=f_z*c_{z-1}+i_z*c_z^{\text{'}}$

(11)$h_z=0_z*tanh\left(C_z\right)$

The LSTM network infrastructure processed the representation vector obtained as an input from the initial to the final data. Let $H?R^{d\times N}$ stand for the matrix containing the hidden vector $\left[h_1,\dots ,h_N\right]$ that LSTM created, whereas the size of hidden state is defined as $d$ and the length of provided data is represented by $N$. Furthermore,$e_N?R^N$ denotes the vector of ls and $v_a$ embedded vector. Figure 2 showcases the infrastructure of the ALSTM technique.

The attention method creates an attention a weight hidden representation $r$, and weighted vector $a.$

(12)$M=tanh\left(\left[\begin{array}{l}{W}_{h}H\hfill \\ W_v{v}_a?e_N\hfill \end{array}\right]\right)$

(13)$a=softmax\left(w^{T}M\right)$

(14)$r=H{a}^T$

In this formula, the presented parameters are characterized by $M?R^{\left(d+d_a\right)\times N},$ $a?R^N,$ $r?R^d.$ $W_h?R^{d\times d},$ $W_v?R^{d_a\times d_a}$, and $w?R^{d+d_a}$. Here, $r$ implies the weight representation of data with a provided feature, and vectors containing attention weights are implied as $a$. The attention system allows the model to capture the important features of data, but assumes various aspects.

Lastly, an AAO algorithm was employed as the hyperparameter optimizer of the ALSTM model. Uymaz et al. [20] developed the AAO technique as a biologically stimulated metaheuristic optimization approach to overcome the problems of continuous and real-time optimization. This is a motivation for the search activity of microalgae. The life cycle included mitotic reproduction, which alters the environmental adaptation, and dominant species. The helical motion phase, adaptation stage, and the reproduction or evolutionary stage are the three stages of the AAO algorithm. The reproduction or evolutionary phase are exploited to replenish the community cell by resurrecting algae by mitotic partition once they have sufficient sources of nutrients and light in the environment. Algae perform a movement called helical movement [21]. Algae cells employ their flagellum (organelle) for helical motion and they exist in a liquid atmosphere and congregate near to the liquid surface where there exists an adequate light source as explained in Algorithm 2.

The AAO approach derives an objective function according to the mean square error (MSE) and it is utilized for predicting the testing output of the ALSTM approach. It is determined by the following equation:

(15)$MSE=\frac{{?}_N^i{\left|y_i-{\widehat{y}}_i\right|}^2}{N}$

In Equation (15), $y$ designates the iteration count, $y_i$ represents the experimental value, and ${\widehat{y}}_i$ signifies the predicted values, respectively.

4. Results and Discussion

This section examines the load forecasting performance of the WHODL-STLFS model under two aspects: an FE grid and a Dayton grid. Figure 3 shows the MAPE examination of the WHODL-STLFS model on the applied FE grid. The figure indicates that the WHODL-STLFS model obtained reduced MAPE values during the training and testing phases.

Figure 4 depicts the MAPE examination of the WHODL-STLFS approach on the applied DAYTON grid. The figure demonstrates that the WHODL-STLFS technique attained decreased MAPE value during the training and testing stages.

Table 1 and Figure 5 provide the load-prediction outcomes of the WHODL-STLFS model with existing models on an FE grid. The experimental values indicated that the WHODL-STLFS model demonstrated enhanced outcomes with minimal differences between the actual and predicted values. For instance, under the conditions of a 1 h duration and 672 kW actual load, the WHODL-STLFS model predicted a load of 671 kW. Likewise, under the conditions of a 10 h duration and 735 W actual load, the WHODL-STLFS approach predicted a load of 733 kW. Similarly, under the conditions of a 24 h duration and 673 kW actual load, the WHODL-STLFS method predicted a load of 672 kW.

Table 2 and Figure 6 offer the load-prediction outcomes of the WHODL-STLFS method with present approaches on the DAYTON grid. The experimental value indicates that the WHODL-STLFS method demonstrated improved outcomes with minimal differences between the predicted and actual values. For example, under the conditions of a 1 h duration and 175 kW actual load, the WHODL-STLFS approach predicted a load of 174 kW. Similarly, under the conditions of a 10 h duration and 213 W actual load, the WHODL-STLFS technique predicted a load of 213 kW. Likewise, under the conditions of a 24 h duration and 179 kW actual load, the WHODL-STLFS approach predicted a load of 178 kW.

Table 3 and Figure 7 demonstrate the comparative EXET analysis of the WHODL-STLFS model on two grids. The experimental values indicated that the Bi-level model exhibited poor performance with increased EXET values. FCRBM, AFC-ANN, and LSTM models exhibited slightly decreased EXET values. Although the MI-ANN model achieved reasonable EXET values, the WHODL-STLFS model exhibited enhanced performance with minimal EXET values of 12.11 s and 10.54 s on the FE grid and Dayton grid, respectively.

Table 4 and Figure 8 illustrate the comparative average error rate (AERR) examination of the WHODL-STLFS method on two grids. The experimental values indicated that the LSTM approach demonstrated poor performance with improved AERR values. Comparatively, the Bi-level approach demonstrated slightly reduced AERR values. Although the AFC-ANN technique accomplished reasonable AERR values, the WHODL-STLFS method illustrated improved performance with minimal AERR values of 4.13 and 2.88 on the FE grid and the Dayton grid, respectively.

Table 5 and Figure 9 illustrate a comparative MAPE inspection of the WHODL-STLFS method compared with other existing methods. The results infer that the WHODL-STLFS technique reached minimal MAPE values. For instance, in 60 samples, the WHODL-STLFS model reached a lower MAPE of 0.164%, whereas the LSTM-AFC-ANN and Bi-level models resulted in increased MAPE of 3.306%, 0.715%, and 2.813%, respectively. Additionally, in 720 samples, the WHODL-STLFS technique reached a lower MAPE of 0.059% while the LSTM-AFC-ANN and Bi-level models resulted in improved MAPE of 2.485%, 0.458%, and 2.392%, respectively.

Table 6 and Figure 10 show a comparative EXET examination of the WHODL-STLFS approach compared with other current techniques. The results showed that the WHODL-STLFS method obtained minimum EXET values. For example, in 60 samples, the WHODL-STLFS technique attained lower EXET of 9.19 s while the LSTM-AFC-ANN and Bi-level methods resulted in improved EXET of 65.56 s, 57.96 s, and 105.79 s, respectively.

Moreover, in 720 samples, the WHODL-STLFS technique obtained a lower EXET of 14.58 s while the LSTM-AFC-ANN and Bi-level methodologies resulted in improved EXET of 72.85 s, 64.93 s, and 111.49 s, respectively. Finally, these results confirmed the enhanced load-prediction results of the WHODL-STLFS model on the SGs.

5. Conclusions

In this study, a new WHODL-STLFS technique was developed for accurate and timely load forecasting for SGs. The presented WHODL-STLFS technique encompassed a three-stage process. At the initial level, the WHODL-STLFS technique used the WHO-based feature selection process. Next, the ALSTM model was exploited to predict load in SGs. Lastly, the AAO algorithm was employed as the hyperparameter optimizer of the ALSTM model. The experimental validation of the WHODL-STLFS technique was undertaken and the results indicated its promising forecasting performance compared with recent models. Thus, the WHODL-STLFS technique can be applied as a proficient approach for load forecasting in real time. In the future, we will extend the WHODL-STLFS technique to validate real-time large-scale SG data to assure its consistent performance. In addition, advanced DL models can be employed to improve the prediction outcome. Moreover, statistical results and ablation studies need to be performed in the future.

Conceptualization, A.M. and E.A.; methodology, A.G.; software, A.S.Z.; validation, E.A., A.G. and R.M.; formal analysis, M.I.E.; investigation, A.A.A.; resources, N.M.S.; data curation, N.M.S.; writing—original draft preparation, E.A., A.G., R.M. and A.M.; writing—review and editing, A.G., E.A. and R.M; visualization, R.M.; supervision, E.A.; project administration, A.M.; funding acquisition, E.A. All authors have read and agreed to the published version of the manuscript.

This article does not contain any studies with human participants performed by any of the authors.

Not applicable.

Data sharing is not applicable to this article as no datasets were generated during the study.

The authors declare that they have no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Table 1

Load-prediction outcome of the WHODL-STLFS system with distinct timeslots under the FE grid.

Load (kW); FE GRID
Timeslots (h)	Actual	LSTM	AFC-ANN	Bi-Level	WHODL-STLFS
1	672	656	647	653	671
2	676	661	665	681	677
3	695	697	686	726	698
4	713	732	737	761	733
5	736	759	775	784	763
6	760	746	793	798	764
7	769	750	776	782	765
8	765	734	747	769	756
9	745	715	726	740	746
10	735	708	717	735	733
11	723	702	703	720	722
12	708	691	710	687	706
13	700	694	700	710	699
14	703	723	729	741	705
15	720	752	750	767	724
16	750	759	772	778	753
17	768	758	783	764	768
18	770	742	776	756	769
19	744	727	765	744	744
20	730	687	729	725	726
21	705	667	691	682	701
22	685	659	670	677	685
23	659	668	659	660	663
24	673	672	631	652	672

Table 2

Load-prediction outcome of WHODL-STLFS system with distinct timeslots under DAYTON grid.

Load (kW); DAYTON GRID
Timeslots (h)	Actual	LSTM	AFC-ANN	Bi-Level	WHODL-STLFS
1	175	167	172	169	174
2	172	172	165	166	170
3	167	160	164	162	169
4	171	157	179	163	171
5	173	174	179	182	178
6	182	184	191	185	188
7	201	196	201	209	203
8	211	204	218	221	211
9	213	212	211	217	215
10	213	212	216	223	213
11	212	206	216	220	210
12	209	204	216	218	208
13	200	204	201	199	201
14	206	201	210	210	207
15	198	192	204	200	199
16	193	193	194	190	196
17	193	187	196	201	190
18	192	187	196	201	192
19	193	191	194	202	197
20	199	199	193	189	202
21	206	202	188	190	200
22	201	197	191	214	190
23	191	188	209	201	179
24	179	169	178	172	178

Table 3

EXET analysis of WHODL-STLFS system with existing approaches under two grids.

Execution Time (s)
Methods

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	Allianz SE - Allianz to invest in 960MW offshore wind farm He Dreiht in the German North Sea
	EnBW - Green light for EnBW He Dreiht offshore wind farm
	Hitachi Energy high-voltage technology strengthens northern Namibian electricity backbone
	Mauritius' IBL buys Kenyan solar firm Equator Energy
	Voltalia deepens loss in 2022
	Power generation falls 4 percent in February
	PM inaugurates 1,650 MW coal-fired power plants in Thar, says desert to witness economic boom
	Hitachi Energy high-voltage technology strengthens northern Namibian electricity backboneHitachi Energy's leading high-voltage solutions will play a significant role in ensuring reliable power sup-ply in Oshakati, Ongwediva, Ondangwa and surrounding
	Latin America's largest wind turbine is already installed in Peru
	The Xunta will value in its permit to renewable projects if they lower the price of electricity to residents
	Scatec ASA - Mendubim solar project contributing to Brazil’s clean energy frontrunner position
	EC clears Romania's EUR-103m battery storage support scheme
	Neoenergia launches 600-MW wind, solar complex in Brazil
	Lagos eyes 1GW by 2030 on new law - Sanwo-Olu
	L&T enters into Electrolyzer Manufacturing Binding agreement with McPhy Energy
	Flexible power technologies will make Africa's energy leapfrogging a reality
	Houston's fast-growing hydrogen industry could get boost with addition of equipment manufacturer
	Government cuts to lower electricity prices force electricity companies to repay 450 million euros already
	What is Sizewell C and how will it be paid for? Campaigners challenge new plant
	Singapore: Oral reply to PQ on ASEAN power grid and electricity sustainability
	Ashoro Hydroelectric Power Station Unit 1 Begins Commercial Operation: Comprehensive Renovations Completed for Plant Operating over 60 Years
	Renewable energies and electricity prices
	UAE presents its national report on nuclear safety
	Why Transcorp Power Seeks Cheaper Alternative To Servicing Power Turbines Abroad
	Prime Minister inaugurates two coal-fired power plants - Press Note issued by Ministry of Information and Broadcasting
	Koeberg Steam Generator Replacements Hit Milestone
	China's new coal plants set to become a costly second fiddle to renewables
	Thar Badle Ga Pakistan is becoming a reality with 3000 MW electricity generated from Thar coal being added to the national grid benefiting the people: Bilawal Bhutto Zardari - Press Release issued by Pakistan People's Party
	Batteries at solar and wind farms: logical but still loss-making
	DENR, First Gen unit ink hydro project agreement
	Lafarge to cover 20% of energy needs in Poland with wind power
	The explosion of renewables, by Robert Tornabell
	India's Commitment To Clean Energy: Laws & Regulations
	Eskom is again back to its rotational load shedding
	EC clears Taiwanese wind business acquisition by Macquarie, wpd
	Iran Aims to Expand Nuclear, Renewables Energy Partnership with Russia: Minister
	China-backed project set to power Uganda
	LS Cable wins 110 bln-won submarine cable supply deal
	Zimbabwe's new 300MW coal-fired plant starts feeding into grid
	Dava wind farm bid hits bump in road
	Iberdrola strengthens ties with Lula and will invest more than 5 billion in Brazil
	Iberdrola to invest 5.3 billion euros in renewables in Brazil
	Elecnor is to Participate in the Largest Energy Project in South Australia
Mar 22
	Shanghai Electric project adds another 1320 MW to national grid system: Khurram
	POWERGRID wins 6 projects through Tariff Based Competitive Bidding
	Finerge buys five wind farms from Onex Renewables, which acquired them from EDP for 548 million
	Energy minister: Negotiations on Zaporizhzhia nuclear plant reach ‘dead end’
	144 power pilferers nabbed in two days in South Punjab
	Brazil exports enough electricity to supply 30% of Uruguay
	Indonesia, US announce partnership on SMR nuclear clean energy
	Equinor, Polenergia finish survey for Polish offshore wind farms
	MQM-P leaders concerned over unscheduled load-shedding in city
	Barents Blue clean ammonia plant project status regarding reservation of power from the local power grid
	NEPRA delegation visits K-Electric
	Partners developing floating substation get backing from Danish scheme
	Japan's JERA to pay $1.6B to buy Belgium's offshore wind leader Parkwind
	China’s Windey to supply turbines for 854 MW wind farm project in Serbia
	Robert Bosch to source 50 MW from He Dreiht offshore wind project
	21 wind power plants in 7 earthquake-hit provinces generating electricity
	(NATIONAL): Prime Minister inaugurates two coal-fired power plants in Thar
	PM directs speedy completion of wind, solar energy projects
	Darlington Wind Farm community gets 30 per cent boost
	Minister Pralhad Joshi reviews construction of NUPPL Thermal Power Plant
	Berlin wind energy talks to focus on fast expansion of turbines
	Eni JV, CIP plan 2 GW of additional floating wind parks off Italy
	India to have a modern and smart power transmission system; Government accepts the Task Force report
	National Grid energise world's first T-pylons
	PSALM says qualified bidders for Casecnan now down to seven
	First Gen unit gets DENR nod on 120-MW hydro facility
	Zeroavia to Partner with Nordic Energy Company Fortum on Hydrogen Hubs at Airports
	Japanese super-insulator facilitates cryogenic storage of hydrogen
	ENEC signs MoU with Romanian nuclear group
	Africa: Koeberg steam generator replacements hit milestone
	Indonesia plans to build thorium-based power plant
	ESB introduces payments at NI's EV charging points
	Firms OK'd to bid for power plant
	Botswana to become net electricity exporter
	EDP starts the process for its first large hydrogen plant
	Clean bill of health for Unit 7. . . Engineers raise output to 150MW
	ESB profits soar to €847m after succession of price hikes
	Swedish energy company selects Sectra's service to protect critical systems against unauthorized access
	Kenya Power blackouts hit ten hours on ageing lines
	Meralco secures 670-MW supply from Aboitiz, SMC
	ERC delays blamed for aborted power deals
	Photon Energy Secures DSR Capacity of 389 MW and Locks-in EUR 24.8 Million in 2024 Revenue
	KKR and Brookfield agree transaction for global renewable developer X-ELIO
	How Ukraine has maintained its energy supply despite the war
	UP SDO who had put up Osama bin Laden’s poster calling him ‘world’s best engineer’ sacked from govt job
	CEZ makes record net income of CZK80.7bn in 2022
	PM to attend COD ceremony of TCB-1 Integrated Power Project on Wednesday
	Everfuel - Q4 2022: Accelerated development of green hydrogen infrastructure
	Meralco eyes rebidding of supply deal scrapped by San Miguel Global Power
	PM inspects progress of Uma Oya hydropower project
	New law paves way for states' power markets
	The fastest solution to nuclear in Nigeria will not be the right one
	83 % of the electricity capacity added in 2022 came from renewable energies
	Hydrogen yes, but not like this
	DGW signs an Agreement with Etihad ESCO to establish renewable power plant
	A government trapped, with no way out?
	Electricity Minister visits Eskom power stations
	World fishery groups sound alarm as Japan presses on with nuclear-contaminated wastewater dump, risking wide-ranging negative effects to marine life
	Intl community has right to demand responsibility from Japan for dumping nuclear-contaminated water: FM
	Engineers raise Unit 7 output to 150MW
	PUCSL enters into agreement with District Secretariates to provide free NVQ 3 for electricians, plumbers and solar technicians
	Japan to lift evacuation orders in 2 Fukushima towns next week
	Drone Based Transmission Line patrolling by POWERGRID
	The wholesale price of electricity in the Far East has increased by 50%
	Howard: It’s time to act on nuclear energy
	Iran minister interested in developing ‘peaceful’ nuclear energy with Russia: RIA
	United Kingdom: £1.8 billion awarded to boost energy efficiency and cut emissions of homes and public buildings across England
	China-backed project set to power Uganda
	Gulf Gunkul eyes state clean energy projects.
	Bloom Energy, SK ecoplant Americas Complete Installation of Fuel Cell Systems at Stamford Health Locations
	Africa: Minister confident load shedding will be resolved
	Colruyt sells Parkwind to Japanese JERA for at least EUR 1.5 billion
	Italy’s Edison eyes 4-GW renewables expansion by 2030
	Corio, partners launch Norwegian offshore wind brand Brigg Vind
	RWE plans significant investment in 2023
	United Nations: New online Guidance for Wind Energy Projects in a World Heritage Context
	RWE AG - RWE underlines commitment to floating offshore wind in the Celtic Sea through new ‘Vision’ document
	GreenIT and Copenhagen Infrastructure Partners to develop three floating offshore wind farms in Italy with 2 GW capacity
	Envision Energy leverages MEMS Sensor Technology by Analog Devices to build smart wind turbines
	India Misses Renewable Energy Target Due To Low Solar Rooftop: Parliamentary Panel
	Rueda announces a law to ensure that the benefits of renewables revert to Galicia
	Eirgrid could spend €270m on offshore consultants
	Egco invests B30bn to raise capacity by 1GW
	Government seeks to promote wind energy in the Inter-Oceanic Corridor of the Isthmus of Tehuantepec
	Genneia obtains financing from FinDev and FMO
	The energy sector calls on the government to withdraw the extraordinary measures approved during the crisis
	Opdenergy will hybridize the solar portfolio sold to Bruc with 500 MW of wind power.
	MoNRE finalises regulations for offshore surveys
	French energy union announces 'targeted power cuts'
	How The Building Sector Became A Model For Green Growth
Mar 21
	Havfram Wind to install turbines at Ørsted`s Hornsea 3 offshore wind farm
	Hidroelectrica, Masdar to invest in renewable energy projects in Romania
	CAQM sets timelines for captive thermal power plants to phase out coal
	"Total nuclear shutdown": Germany to switch off last 3 nuclear power plants on April 15
	Mizoram To Build Five Hydropower Plants With Total Capacity Of 800 MW
	Minister Pralhad Joshi reviews construction of NUPPL Thermal Power Plant
	Ilmatar closes buy of 230 MW of Finnish wind projects
	Green light for 150-MW solar PV project in Montenegro
	Power Foundation and Climate Policy Initiative India Pvt. Ltd. sign MoU to advance Sustainable Development in Power Sector
	Poland doubles renewable energy from micro-installations in 2022
	4,498 MW capacity added to CPPA-G system in FY 2021-22
	Acwa Power in deal to develop green projects in Uzbekistan
	Canadian Solar Reports Fourth Quarter and Full Year 2022 Results
	Africa: Electricity Minister at Kusile Power Station
	Ei's assessment: No reduced grid fee for small-scale production plants
	Edesur, the electric utility company, is being intervened for 180 days due to power outages.
	Parliament, ECG hold crunch talks to avert 'dumsor' in the House
	Brookfield unit to take full ownership of Spanish X-Elio
	Traders appeal to Hesco CEO to prepare load management schedule
	ENGIE partners with CarbonClear to finance the access to energy challenge in Africa through the Voluntary Carbon MarketThis partnership will provide measurable carbon finance to impactful solar off-grid deployments throughout the marginalised areas of
	Eskom in court to defend its role to supply uninterrupted electricity
	Czech power company CEZ reports eightfold profits increase
	Foundation laid for country's biggest power grid station
	PM allows handing over of 10 Discos to provinces
	All set for commissioning of new generator to boost Zimbabwe's power supplies
	Brookfield buys KKR out of Spanish renewables developer X-Elio
	DOE: Luzon grid could see 15 weeks of yellow alerts
	BOTSWANA TO BECOME NET ELECTRICITY EXPORTER
	Argentina intervenes Italian-owned power company after massive power cuts
	Indonesia plans to build thorium-based power plant
	Pesco notifies power suspension for Peshawar, Tarbela
	Will the North Sea have a hydrogen grid?
	MENDIRATTA: How data centres can help give back to Africa's electricity grid
	GCC ‘must integrate supply chains for energy security’
	RusHydro: At the construction of the Tower SHPP, the construction of the head unit has begun
	Ellomay Capital Announces Entry into the US Solar Photovoltaic Market
	New nuclear player looks to enter the UK
	South Korea coming for a slice of Africa's emerging nuclear power market
	Iran interested in developing nuclear energy cooperation with Russia - Iranian Minister
	Experts proffer solution to energy crisis in Nigeria
	VEO seals its first solar energy deal at pioneering plant in Rauma
	Turkmenistan’s Energy Ministry opens tender for construction of combined power plant
	Electricity minister says load shedding reprieve ‘has nothing to do’ with EFF national shutdown
	Bengal government plans to buy power from Assam
	Japan's nuclear-contaminated wastewater dump plan sparks concerns on imported food safety
	Xcel Energy admits its nuclear power plant leaked 400,000 gallons of radioactive water
	MIL-Evening Report: Australia’s electric vehicle numbers doubled last year. What’s the impact of charging them on a power grid under strain?
	Nuclearelectrica - Emirates Nuclear Energy Corporation ink MoU to develop nuclear energy programme
	Launch of Brigg Vind Heralds New Era for Norwegian Offshore Wind Power
	Siemens Gamesa to manufacture 132 recyclable blades for Sofia offshore wind farm in the UK
	RWE plans significant investment in 2023, beating 2022’s €4.4 bn
	Baltic Power can borrow EUR 1bn under Poland's offshore wind plan
	Eskom in court to defend its role to supply uninterrupted electricity
	ACWA Power to Develop 1GW Renewables Project in Kazakhstan
	IEX, JSW Energy, Tata Power shares: Which power stocks to buy on peak summer demand?
	UK’s wind farms produce 26.01% of all electricity on Mar 20
	Vestas bags 100 MW of turbine deals in Germany, Finland
	EnBW and the DLR present research project on the use of transport drones for wind farms at the Offshore Drone Forum #2
	Scheduled to be inaugurated, solar power plant on Caximba landfill will be a sustainability milestone for Curitiba
Mar 20
	Gujarat, Tamil Nadu Take Lead In Wind Power Generation
	Minister Pralhad Joshi reviews construction of NUPPL Thermal Power Plant
	Amarenco enters Spain with the purchase of its first solar plant from Hanwha
	21 wind power plants in 7 earthquake-hit provinces generating electricity
	Rampal plant resumes power generation after month-long closure
	Russia to help African countries with electricity production - Putin
	Electricity Minister Visits Eskom Power Stations
	Singapore facilitates renewable power imports from Cambodia, Indonesia
	Thar coal offers sustainable solution to energy issues of country: Imtiaz Sheikh
	Axpo launches wind power activities in Finland
	China's Renewable Energy Expertise Can Benefit Pakistan: Experts
	K-Electric’s improved efficiency after privatisation, an example for power companies: PM
	Seabased plans 10-MW wave power project in Tonga
	A year on, Ukraine's embattled nuclear plant turned Russian 'military base'
	India to have a modern and smart power transmission system; Government accepts the Task Force report
	Arzybek Kozhoshev: "EAEU countries are ready to jointly overcome challenges in the energy sector".
	EMA Grants Conditional Approval for 1 Gigawatt (GW) of Electricity Imports from Cambodia
	Landi Kotal residents besiege grid station to protest outages
	TNB accelerates responsible energy transition drive, partners Thailand’s energy players
	Doosan Enerbility Signs KRW 1.15tn Contract for Combined Cycle Power Plant in Kazakhstan
	Eskom suspends load shedding until Tuesday
	India: GREEN HYDROGEN MISSION - Expected to reduce 1 lakh crore worth of fossil fuel imports and nearly 50 MMT per annum of CO2 emissions by 2030 – Union Power & NRE Minister
	Infinity Power Finalizes Acquisition of Lekela Power in Africa's Biggest Renewable Energy Deal
	Does installing a solar PV system boost your home’s value?
	TEPCO Power Grid: Regarding the report to the Ministry of Economy, Trade and Industry on "Response to ensuring neutrality and reliability in general power transmission and distribution companies (urgent order)"
	Local flexibilities: Enedis launches a new call for tenders to contribute to the performance of the electricity network
	Tokyo Electric Power: Notification of "decommissioning implementation plan" based on the decommissioning reserve fund system
	Understanding South Africa’s Energy Crisis (By NJ Ayuk)The frequent and extended power outages taking place have left businesses in Africa’s most industrialized country struggling to function
	Photon Energy secures EUR 22m for Romanian PV projects
	Govt flexing muscles to manage power supply in summer
	Mainstream Renewable Power and Actis complete sale of Lekela Power
	Meralco races to augment 1,800 MW of crucial energy after SMC exit
	ZETDC struggles to pay regional power suppliers
	Eskom’s failure takes centre stage at high court
	UK First: Lhyfe and Centrica to develop offshore renewable green hydrogen
	HydrogenPro US expansion with 500 MW manufacturing facility in Texas, US
	Investment in renewables doubled in Andalusia to 1.3 billion euros by 2022
	Eskom’s NERSA-approved 18,49% electricity price hike to municipalities is unaffordable and unjust
	Hydrogen advances: which cars use it and how does it work?
	Stakeholders hail Buhari on law to allow states generate, transmit electricity
	Uganda to scrap take-or-pay contracts in electricity industry
	Collaboration with UCLA: Research on Perovskite-CIGS tandem solar cells yields 24.9 percent efficiency
	In briefState opts for veto power on spent nuclear fuel
	Taiwan's electricity prices to increase 11% in April
	Tractebel and NRG|PALLAS Join Forces to Support Nuclear New-Build in the Netherlands
	Can coal be a clean energy source? Australia and Japan think so
	Unit 3 of Norochcholai Power Plant suffers breakdown
	Energy rich present our gift to the future
	German gas network suitable for hydrogen
	Kazakhstan, UK talk co-op on green hydrogen production
	Spain asks Brussels to exclude hydrogen produced with fossil energy from renewables
	Cummins and Accelera fuel hydrogen commitment at Con Expo
	Colombian ecopetrol creates subsidiary focused on technology and innovation in spain
	De Heus and Bel Ga fulfill commitments to sustainability with solar rooftop portfolio
	EDP creates two companies with Aboño and Soto power plants to launch hydrogen investments
	Denmark: Five municipalities leading investors of renewable energy
	Wind Canon winds are blowing
	African renewables platform Lekela moves to new owner Infinity Power
	Wind energy projects produce 64.54 bn units of electricity in India
	Infinity Power Finalizes Acquisition of Lekela Power in Africa's Biggest Renewable Energy Deal
	Lhyfe, Centrica team up on offshore green hydrogen in UK
	enercity is driving the expansion of wind energy in Lower Saxony
	Infinity Power acquires Lekela Power
	India: 64.54 billion units electricity produced from Wind Energy during April, 2022-January, 2023 – Union Power & NRE Minister – Shri R. K. Singh
	Transfer of China's renewable energy expertise to Pakistan urged
	Zim mulls energy efficiency policy
	Fugro and Petrobras pioneer remote subsea inspection survey in Brazil
Mar 19
	German government confirmed "nuclear blackout" for April 15
	Africa: Presidency finalising Electricity Minister’s powers
	The bulk of the Brussels electricity reform is in force in Spain without having avoided the price crisis.
	Tackling Ice on the Baltic Sea to Generate Additional Costs for Offshore Wind Farms
	43 million tons of annual wind turbine blade waste
	Spain subsidized 600 million in electricity to Morocco after the change of position on Sahara
	Could New Zealand become the SAUDI ARABIA OF WIND?
	V2G Charging: Unlock the Benefits of Smart Charging for UK EV Owners
	European wind industry agrees to produce 36,000 megawatts per year
	Ukraine builds a mega wind farm
	Thai company wins grant for region’s largest cross-border wind farm in Laos
	Electricity in Spain 34% cheaper than in France
	More than 90 thousand Edesur users are still without power in AMBA
	Sweden: Electricity consumption down by 6.5% in February 2023
	Management committee meeting of China Southern Power Grid – University of Macau Joint Laboratory of Smart Power and Energy for Carbon Neutrality held at UM
	India to have a modern and smart power transmission system; Government accepts the Task Force report
Mar 17
	China Southern Power Grid Co., Ltd issued RMB 3 bln short-term commercial paper
	Shandong Nuclear Power Company Ltd. issued RMB 1 bln short-term commercial paper
	China Longyuan Power Group Corporation Limited issued RMB 2 bln short-term commercial paper
	MEPCO recovers over Rs 887m from permanent defaulters
	NTPC ranked as the top Independent Power Producers and Energy Traders Globally by S&P Platts
	Masdar talks its plan of activities in Azerbaijan
	Rosatom's subsidiary preparing project to build small HPPs in Congo - Russian ambassador
	NEPRA organizes webinar on incident investigation
	PM directs measures to improve power transmission
	MEPCO disconnects all WASA connections over non-payment of dues
	Thar coal offers sustainable solution to energy issues of country: Imtiaz Sheikh
	EU to auction E 800 million in aid for domestic green hydrogen producers
	Industry associations not quite happy with EC's legislative proposals
	Lithuania approves requirements for maiden offshore wind tender
	Mitie Group plc - Four important lessons on the road to decarbonisation
	Abiding by dept rules, regulation ensures transparency
	BIDEN ADMINISTRATION SETS FEDERAL LIMITS ON INTERSTATE AIR POLLUTION
	Rampal plant resumes power generation after month-long closure
	Monster of Rs2.6tr circular debt will stay despite levying of Rs3.23/unit surcharge
	Russia proposes 'direct payment' for Bangladesh power plant amid SWIFT ban
	SMITH SLAMS SHODDY WIND FARM APPROVAL PROCESS, RENEWS CALL FOR PAUSE
	Greenvolt buys into Solarelit to enter Italy's DG solar market
	The DEA issues permit for preliminary site investigations in the site Nordsøen I
	Learn about the requirements for the new CFE contract | Documentation | Pricing
	Equinor, Petrobras agree to evaluate seven offshore wind projects in Brazil
	UK to spend $24 billion on domestic carbon capture projects to hit energy transition goals
	Vestas Wind Systems A/S - Vestas secures 50 MW order in Finland
	ENERGY CRISIS - There are greener, healthier ways to respond to Kusile pollution problem
	'Strange situation' as electricity prices to soar 14% in NI despite drop in costs
	Building sector plan calls for solar on all new EU buildings
	A North Sea energy megaproject will rid the EU of US LNG
	Volkswagen previews what its future electric utility vehicle will look like, with clear Golf inspiration
	Offshore wind power to play an important role in Vietnam's future
	Ayala unit acquires US wind power assets
	BayWa r.e. and NORD/LB complete financing of solar farm
	India faces high risks of power cuts after years of coal, hydro power neglect
	Kenya hit peak electricity demand in December
	Salt battery attracts many spectators at solar trade show
	LONGi ranks again in BNEF Tier 1 list for Q1 2023 with its PV modules recognized by the industry
	Wind farms produce 49.10% of UK power on March 16
	Nuclear power Taiwan's only hedge against Chinese naval blockade: Peter Zeihan
	Government prioritises renewable energy investment
	Cabinet Welcomes Launch of Fund to Address Energy Challenges
	Magnora ASA: Helios Nordic Energy AB hands over another 21 MW to customer
	Bord refuses permission for Carlow wind farm
	Govt to build 2,000MW nuclear power plant
	Visayan Electric set to cut power rate anew
	Hydrogen power for delivery drones: Wingcopter and ZAL GmbH start joint development
	European Union boosts green technologies to respond to U.S. and China
	EBRD's renewable energy project in Central Asia is largest to date
	EU sees drop in CO2 emissions, green electricity tops gas - IEA
	INPEX to expand its geothermal, offshore wind power business
	Uzbekistan discloses energy generated by SPPs in Navoi and Samarkand regions
	Siemens energy sells shares for $1.33 billion to finance gamesa's offering
	EU envisages subsidies for hydrogen produced with energy - draft
	European Commission Proposes Reform of EU Electricity Market Design to Boost Renewables, Better Protect Consumers, Enhance Industrial Competitiveness

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