Solar Power Prediction Using LSTM_WaOA Model

The increasing penetration of solar power in power systems necessitates accurate generation forecasts to ensure reliable and economical operation. Several forecasting models like time series, machine learning, and deep learning models are used for solar generation forecasting. Deep learning models like LSTM show superior performance among these models. However, the forecasting accuracy of the LSTM model heavily relies on hyperparameters. This necessitates hyperparameter optimization using a suitable optimization algorithm. Therefore, this paper proposes a new hybrid LSTM model called LSTM_WaOA where hyperparameters are optimized using the Walrus Optimization Algorithm (WaOA). The LSTM is known for its three-layer architecture and is chosen for its effectiveness in handling and understanding long-term patterns and dependencies in data. The choice of WaOA is motivated by its proven superiority over different metaheuristic algorithms. This algorithm is crucial in optimizing hyperparameters, ensuring the LSTM model is finetuned to deliver the most efficient and accurate forecasting. The performance of the LSTM_WaOA model is evaluated by a seasonal analysis. Also, the performance is compared with traditional LSTM and ANN models. The results demonstrate that the proposed hybrid model outperforms the reference models.