Nowadays, solar power generation has increased significantly compared to other types of renewable energy. Natural variations, on the other hand, make solar energy production unstable
In most cases, a deep learning algorithm is used to convert weather data into power in solar power forecasting models
To enhance solar power forecasting accuracy, the HHO (Harris Hawks Optimization) technique is applied
Wang et al.,
Yu et al.,
Wang et al.,
Li et al.,
AbdelNasser et al.,
Hossain et al.,
Aslam et al.,
Zhou et al.,
This study has the following gaps that are identified and to fill this gap
To provide the short term solar power forecasting model based on effective feature selection method that is not used in the literature.
In our finding Short term PV power that is 15 min and 60 min interval is used which is not used in this study.
We used a three year solar power datasets to next year power forecasting.
The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy.
This study used a standardized approach for preprocessing a realnumerical solar power datasets. Feature selection in this research uses the Whale Optimization Algorithm. The enhanced LSTM method is used to determine the accuracy of solar forecasts shown in
The Austria and Germany countries data sets are utilized in the solar power forecasting. Each of these countries is taken across two time periods, with the first period lasting 15 minutes and the second lasting 60 minutes. Using the two nations mentioned above, the same time frame.
The proposed method's inspiration is initially explored in this section. Then there's the mathematical model.
This section introduces a mathematical model for surrounding prey, spiral bubblenet feeding technique, & prey search. Following that, the WOA algorithm is suggested.
Humpback whales are capable of detecting prey & encircling it. A WOA technique implies that the current best candidate solution would be the goal target as well as extremely close to it since the position of an optimum design within the search space is unknown. Those specific search agents can attempt to enhance overall positions in comparison to other search agents, as determined by the best solution found. This behavior is represented by the equations below:
Where,
t is the current iteration,
u' is the position vector of the best solution identified,
  is the exact value and is a Multiplication of elements one by one It's worth noting that u' must be changed in every cycle if a better option is discovered. The vectors P and Q are calculated as follows:
where
The reasoning behind
There have been two methodologies created to statistically measure humpback whale bubblenet behavior:
1. Encircling mechanism shrinking: In
2. Spiral updating position: This approach begins with computing the distance here between the whale (u, v) and the prey (u', v'), as illustrated in
where
While swimming, grey whales swim in a lowering circle around their food in a spiral pattern. We assume there's a 50/50 chance of either employing the shrinking encircling mechanism or the spiral model to update whale locations during optimization to simulate this simultaneous behavior. The following is the mathematical model:
p indicates random integer between [0,1].
Humpback whales hunt for food at random times, in addition to employing bubble nets. The following is the mathematical model for the search.
A similar strategy that involves changing the B vector can be used to locate prey. In reality, based on their relative locations, humpback whales seek at random. As a consequence, we use B to move the search agent away from the reference whale by using random values larger than or equal to 1. Rather than employing the best search agent identified so far, we utilize a randomly generated search agent that can adjust the position of a search agent throughout the exploration phase. That strategy, combined with the fact that B > 1, The mathematical model looks like this:
Where
In
To make searching and attacking easier, a parameter is decreased from II to 0. When P > 1, a random search agent is chosen, however since
Because it includes exploration/exploitation capabilities, WOA may theoretically be called a global optimizer. In addition, the proposed hypercube approach establishes a search region around the best answer, allowing additional search agents to use the current better details within that region. The WOA algorithm can quickly transition between exploration and exploitation due to adaptive changes in the search vector A: by reducing
Although we might have included mutations and other evolution in the WOA formulations to completely reproduce humpback whale behavior, we elected to simplify the WOA algorithm by reducing the amount of heuristics and internal parameters. On the other hand, hybridization utilizing evolutionary search techniques might be the subject of future research.
The LSTM network is a form of RNN that combines representation learning and model training without the requirement for extra domain knowledge.
LSTM is specifically developed to address the problem of gradient vanishing, which makes it difficult to retain the short and longterm correlation between vectors. We also looked at the influence of single parameter optimization on the proposed approach and found that only learning rate optimization had little impact on the proposed LSTM's performance. The suggested LSTM's overall performance improves when the learning rate, momentum rate, and dropout rate are all optimized together. Here, the weight of the LSTM is obtained by HHO.
This technique appears to be a metaheuristic optimization method. It replicates Harris Hawks' cooperative "surprise pounce" behavior Exploitation and Exploration levels are present in the HHO strategy, as they are in other metaheuristic algorithms. HHO is a populationbased optimization method that does not use gradients. As a result, when properly formulated, it can be used to resolve every optimization issue. In the HHO algorithm, exploration have2 stages, and exploitation have 4 stages. There are two exploration stages and four exploitation steps in the HHO algorithm. This cooperative behavior's mathematical model also presents a novel stochastic technique for tackling a number of optimization issues. In the following section, to suggest a DVR control scheme, the HHO approach is applied.
We simulate the proposed HHO's exploring and exploitative stages in this part, which are based on Harris hawk prey investigation, surprise pounce, as well as a variety of attack methods. HHO can solve an optimization issue with the appropriate formulation because it is a populationbased, gradientfree optimization strategy.
Throughout this stage, HHO roams around in random places, looking for prey utilizing one of two methods. To alter the location of each hawk, an equation is utilized (
Where:
K, c1, c2, c3, c at each cycle, are the random integers inside (0,1)
The hawks arrive at an average location using the
where:
n denotes the number of hawks (n=10 here due to the multitude of search engines).
Because the prey tries to run, the change between exploitation and discovery, and the transition from searching to attacking takes place. The victim expends a great deal of energy striving to escape.
where:
Before swooping down on the victim, the HHO softly circles it to exhaust it. This action is explained by
Where,
In iteration t,
K: the strength of the prey when bouncing randomly during the escape is known as k=2 (1c5). To imitate the natural behavior of prey, this value fluctuates at random during each cycle.
R: is the escape prey possibility.
In this situation, the victim is unable to depart due to exhaustion. As a result, hawks have an easier time catching and pinning their prey. Each hawk takes use of its existing position to improve its circumstances (
Imagine that hawks can use the following rule to analyze (decide) their next action.
The HHO technique uses the LF (Lévy Flight) principle can build the differential equation to mimic the prey's zigzag motion when attempting to elude. There under the LF principle, hawks should dive for their prey
Where, d is denoted as the problem's dimensionality is denoted a 1
By minimizing the distance between the average location as well as the prey location, team members' whereabouts are revealed.
If h and g were calculated using the additional criteria
Where,
The performance metrics of RMSE and MAE are acceptable. Three approaches are compared: the suggested method, the traditional LSTM, and the SVM.
Analysis of solar power in sunny day V/S time is shown in
Analysis of solar power in sunny day V/S time is shown in
Analysis of solar power in sunny day V/S time is shown in









Training process 
RMSE (MW) 
3.07 
15.87 
259.95 



MAE (MW) 
6.62 
10.28 
172.07 


Testing process 
RMSE (MW) 
3.24 
15.86 
262.63 



MAE (MW) 
7.03 
9.7 
168.81 


Training process 
RMSE (MW) 
5.61 
19.86 
259.87 



MAE (MW) 
18.64 
20.52 
191.52 


Testing process 
RMSE (MW) 
5.87 
17.36 
260.12 



MAE (MW) 
19.63 
22.79 
187.33 


Training process 
RMSE (MW) 
13.53 
212.66 
8212.8 



MAE (MW) 
120.39 
144.82 
4892.79 


Testing process 
RMSE (MW) 
13.26 
211.96 
7403.08 



MAE (MW) 
115.89 
141.71 
4210.66 


Training process 
RMSE (MW) 
21.18 
520.64 
8217.02 



MAE (MW) 
269.52 
332.2 
4833.61 


Testing process 
RMSE (MW) 
21.86 
535.22 
7356.05 



MAE (MW) 
279.85 
342.43 
4109.6 
In this study, we describe a hybrid technique for enhancing the accuracy of solar power forecasts over short periods. We used a genuine numerical solar power dataset and a conventional preprocessing method for our study. The Whale Optimization Algorithm is used to pick features in this study (WOA). The accuracy of solar power estimates is determined using an LSTM (Long ShortTerm Memory) approach. The HHO (Harris Hawks Optimization) method is also employed to increase the accuracy of solar power forecasts. The findings imply that the suggested method considerably enhances the accuracy of shortterm solar power estimates. Results were examined, and the recommended method was implemented in Python. Solar energy in sunny day versus time solar energy in sunny time series for 15 minutes – Austria, in this situation, the LSTM and SVM are at their highest values when compared to other 800 MW times 10:00:00. Proposed, traditional LSTM and SVM for Austrian solar power sunny time series data for 60 mints. When compared to other users who use more than 25000 MW, proposed is the peak value at 11:06:40. When compared to other data of 20,000 MW, the actual data are in the low to midterms. data from Austria for 15 minutes comparing solar power on cloudy days to time. To compare to others who use more than 400 MW, the suggested peak time for conventional LSTM and SVM in this situation is 16:40. The conventional LSTM reaches its maximum value at 05:33:20 for 60 minutes of Austrian data on solar power in cloudy days.
German data for a 15min period on solar power in sunny days versus time. At 11:06:40, the conventional LSTM reaches its maximum value. Solar energy on a cloudy day Traditional LSTM and SVM are at their peak when compared to others above 12000 MW during the survey of solar power over a 60min period in Germany. 1200MW of solar power feature predictions for 2020 are based on solar power VS datetime solar power time series. This is a lot less than bench marking errors. Future research will look at how well the suggested method predicts additional renewable energy sources, like the amount of electricity generated by wind farms.