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The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh. [ABSTRACT FROM AUTHOR]

This paper represents a new framework to forecast electricity power net-load in renewable energy systems. Estimating electricity power net-load with high accuracy affects economic well-being, stability, and security of power networks. Despite this, large nonlinear variations make its estimation difficult and complicated. In this paper, a new, simple, robust, and straightforward method is proposed to predict signals with volatile characteristics to satisfy this need. This framework combines deep learning of the multi-input LSTM network type with fuzzy system and discrete wavelet transforms. Wavelet-based transforms provide insight into hidden details and aid in forecasting points with high chaos. Furthermore, technical indicators provide a way to determine the trend and momentum of data and to select the optimal time frame for estimation. Finally, the real case of Austrian data is selected, and the electricity power net-load is estimated. According to the results, the proposed framework can forecast the electricity power net-load with 97.7% accuracy. Furthermore, the forecast accuracy is improved to 99.5% by using wavelet transforms and fuzzy system simultaneously in the forecasting process. [Display omitted] • Electricity power net-load forecasting in renewable energy systems. • Combining deep learning with fuzzy and discrete wavelet transforms. • Improving the forecast accuracy to 99.5%. [ABSTRACT FROM AUTHOR]