1. Introduction
Renewable energy is a term used for energy sources, such as solar, wind, biomass, geothermal, hydroelectricity, and wave energy. Renewable energy systems are a set of systems used to meet the electricity needs due to the decrease in resources, such as natural gas and coal. Fossil fuels and their derivatives, which are frequently used in electricity generation, not only pollute the environment but also have negative effects on human, animal, and plant health. For similar reasons, the use of renewable energy is important. The demand for these existing renewable energy resources is increasing day by day. However, not enough studies on energy efficiency have been conducted. This situation creates a significant obstacle against fossil fuels. The initial installation and maintenance costs of solar power plants are high. However, considering that their average lifespan is 25 years, the issue of efficiency is significant for solar power plants. There are many studies on solar energy systems [
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5]. Wang and his colleagues worked on estimating solar energy using power data and meteorological data. In their studies, they proposed three different methods: convolutional neural network (ESA), UKSB, and ESA-UKSB hybrid model. The performances of the forecast models were compared using datasets created with six-month periods [
6]. In their study, Yıldız and Açıkgöz proposed a Kernel overlearning machine-based method for solar energy estimation. The proposed model demonstrated superior prediction performance compared to the extreme learning machine (ELM) and Levenberg–Marquardt-based methods used as a comparison method. This was proven by performance criterion values [
7]. In their study, Li and his colleagues proposed a hybrid model created by the combination of the Wavelet Packet Decomposition (WPD) method and UKSB for solar energy forecasting. The power data obtained from the power plant were separated into their components via the WPD method. Performance evaluation results showed that the proposed hybrid model is more successful than models based on multilayer sensors [
8]. In their study, Behera and his colleagues proposed three different hybrid methods obtained as a result of the combination of particle swarm optimization methods and the ALM method. Additionally, the GYSA-based method was used as a comparison method. Three different datasets were obtained using solar energy output power data measured at 15, 30 and 60 min intervals. The results obtained at different time horizons for the short-term prediction of solar energy showed that the hybrid model, which is a combination of accelerated particle swarm optimization and ALM, exhibited superior prediction performance compared to other models. Proven by performance evaluation results, it has been stated that hybrid ALM models give better prediction results than the comparison method for all datasets [
9]. In a study by Su et al., the last ten neural networks were analyzed for short-term PV prediction. It was suitably calibrated and evaluated on a one-year dataset of a 406 MWp PV plant in the UK. Additionally, a new hybrid forecasting strategy derived as the sum of the best-performing forecasting models is proposed and evaluated. They concluded that among all the methods, the proposed hybrid model generally showed the best performance [
10]. Mishra et al. proposed a WPD-UKSB-based method in their study. With the WPD method, time-series solar energy data were separated into different frequency series and used in the dataset. The dataset was organized in two different ways. The results obtained to predict the time horizon one hour ahead showed that the proposed deep learning-based method had better prediction performance than the machine learning-based methods used as a comparison method. This has been proven by performance evaluation results [
11].
Forecasting wind energy is of vital importance in the process of determining energy production strategies. However, this process covers a very complex and challenging application area. Researchers have adopted various methods to overcome this challenge. At the same time, they have constantly tried to develop new approaches [
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18]. In a study conducted by Tan, a hybrid prediction model consisting of four modules, clustering, data pre-processing, evaluation, and prediction, was proposed. Performance analysis was made by comparing the results obtained with the proposed model with the results of some traditional prediction models [
19]. In a study conducted by Görgel and Kavlak, hybrid CNN and LSTM methods were used, and energy production estimates were made. Apart from the hybrid method, the CNN, LSTM, and GRU methods were applied one by one for comparison purposes, and the results were shown graphically. The mean square error in production prediction made with the developed hybrid CNN-LSTM learning structure was reduced to 1.17 [
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21,
22]. Transmission Systems Operators (TSOs) in Europe are helping power plants develop their planning strategies by publishing wind forecasts ahead of daily market clearing. Toubeau and his colleagues developed a new deep learning method with the idea that more effective results can be obtained by combining the prediction results of more than one model for large-scale planning [
23]. Azad and colleagues developed two fundamentally different approaches, namely statistical and neural network-based approaches, to predict the following year’s hourly wind speed data. They used a set of recent wind speed measurement samples from two meteorological stations in Malaysia, namely Kuala Terengganu and Mersing, to train and test the dataset. The results obtained with the proposed method gave very promising results in terms of a very small mean absolute error (MAE) [
24]. Tascıkaroglu and his colleagues used wavelet transform (WT) to decompose wind speed data into more stationary components. They later proposed a new wind speed estimation method that uses a spatiotemporal model in each subseries to include both temporal and spatial information [
25]. To improve the accuracy of wind speed and wind power prediction, an ultra-short-term wind speed prediction model based on time-scale recognition and dynamic adaptive modeling is proposed. It shows that the proposed model offers higher accuracy than benchmark models that use the same prediction algorithms without considering the time scale and data sweep [
26]. Wang et al. proposed a multi-mode multi-task transformer network (M2TNet) model that can achieve ultra-short-term wind power multi-step forecasting based on multi-source heterogeneous data, which confirmed that the M2TNet model performs better in terms of prediction accuracy and computational efficiency [
27]. Wind speed measurement is not always performed on land. Wind speed forecasts are also made at ports. By estimating wind speed in ports, port operations are ensured on time, and efficiency in energy use is increased. Gu and colleagues proposed a wind speed prediction model based on the Sparrow Search Algorithm (SSA) optimized Wavelet Neural Network (WNN) to obtain high accuracy and fast prediction of harbor wind speed. To verify the effectiveness of the proposed method, the model is validated using wind speed data measured from the Chuanshan Port Area of Ningbo-Zhoushan Port during 2022, and its performance is compared with three other models: SSA–BP, SSA–LSTM, and WNN. It reveals that the proposed forecasting model has a good performance in port wind speed forecasting and outperforms other comparative models in terms of forecasting accuracy and convergence speed [
28]. In their study, Lin et al. used the temporal convolution network (TCN) algorithm to predict long-term wind power with a MAPE of less than 10%. In the study, a MAPE value of 5.13% was obtained for a 72 h wind power forecast. Finally, measurements were made with the LSTM, RNN, and GRU models for power estimation and compared with TCN results [
29].
In this study, forward-looking electricity production predictions were made using renewable energy sources, artificial intelligence, and deep learning. In our study, the LSTM and SARIMA models, one of the deep learning methods, were used. Three different regions with an annual production capacity of 1 MW were selected as examples. They were analyzed over three SPPs. As for wind energy, wind speeds in two different places were estimated. As a result of this study, weekly, monthly, and annual predictions were made. Tests were conducted with the MAPE, R2, and RMSE error performance measurement systems. The LSTM, GRU, CNN-LSTM, CNN-RNN, and LSTM-GRU deep learning methods were used in this study. The CNN-GRU model achieved high success in wind energy forecasting.
The most important advantage of this study is the dataset. A unique and new dataset was prepared for this study. The dataset was obtained from three different real solar power plants. Three years’ worth of data was taken to achieve high accuracy in this study. Considering that the lifespan of solar power plants is 25 years, 3 years of data is thought to be at the optimum level. Two different models and five different performance evaluation measurement systems were used in this study. In this way, a high accuracy of 89% was achieved. The energy efficiency of power plants will decrease over the years. By estimating the production values of the power plant, it is possible to carry out plant maintenance at the most appropriate time and at a low cost.
The structure of this article is as follows:
Section 2 introduces the principles of LSTM and SARIMA, which are solar and wind speed prediction algorithms, the steps of the optimization algorithm, and the datasets.
Section 3 estimates the 2023 energy production of solar power plants at three different points and the annual, monthly and weekly energy production of wind power plants at two different points. It analyzes the prediction results and verifies the effectiveness and superiority of the proposed method. Finally,
Section 4 concludes this paper.
4. Conclusions
Electricity production estimation was made based on solar power plants. In this study, emphasis was placed on time-series analysis using the deep learning methods LSTM and SARIMA. These methods were applied to three different SPP datasets, and the results were compared using RMSE, MSE, NMSE, MAE, and MAPE, which are frequently used in performance measurements.
Tests of Plant_A, Plant_B, and Plant_C were examined, and realistic results were obtained using both methods. The LSTM results achieved successful values compared to SARIMA. While the LSTM Library used in this study benefited from GPU support, the SARIMA library only benefited from the CPU. Thanks to the computing power of the GPU used in this study, it made positive contributions to obtaining the desired values, both in a short time and consistently.
The wind speed data used in this study were taken at a meteorology station. Three-year data were taken from the meteorological station. Using these data, the error, monthly, and annual estimates were made. In this study, a comparison was made with five different deep learning models in the literature, using CNN-LSTM, CNN-RNN, LSTM-GRU, GRU, LSTM, and CNN-GRU. The performance of the prediction results was evaluated according to MAPE, R2, and RMSE performance criteria. In the results obtained in the weekly wind speed forecasting process, it was observed that the LSTM-GRU model showed more accurate and reliable results. According to the obtained performance criteria, it is clearly seen that the proposed LSTM-GRU model performs better than other models in monthly wind speed forecasts. In the annual wind speed forecasting study, the LSTM-GRU model performed better in terms of forecasting. Looking at the performance criteria results obtained (MAPE: 0.5348, RMSE: 0.1095, and R2: 0.9981), they were determined to have high regression values compared to other models.
In a future study, we will aim to expand and improve the developed model and reduce energy production forecasts to a monthly period. In this way, it can be used to quickly detect problems encountered in the same monthly periods in previous years. Energy production in power plants is handled monthly. Electricity production targets can be achieved by making them according to models. The most appropriate time for maintenance can be determined by making energy production estimates. In this way, time and costs for maintenance can be reduced.