Optimized ANN–LSTM Approach for High-Precision Day-Ahead Photovoltaic Power Forecasting

Abstract

The growing integration of PV systems in the power grid requires an ideal forecasting approach of energy management. One of the new concepts that are presented in this research paper is the possibility to forecast photovoltaic power generation 1 day before with the assistance of long short-term memory (LSTM) algorithms which will provide the adequate possibility to predict the future photo generation of power, offer the opportunity to distribute resources much quicker and reach a better result in the utilization of the resources of the renewable energy. It discusses the limitations that relate to traditional forecasting models and presents that LSTM is a great answer to the problem because of its possibility to capture the long-term dependency of time series records. The section on methodology explains how the LSTM networks were implemented, the approach used to preprocess the data, the structure of the model and the training process. This paper has highlighted the role of appropriate selection of input features like historic solar exposure, temperature and past-historical power generation information in positive prediction. The trained LSTM-based forecasting model is validated and evaluated on actual photovoltaic power generation data. Measures of performance The following are applications of the measures of Mean Absolute Error (MAE) Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) mobilize, using, measures of HFES performance. Results show that the LSTM solution is superior to the traditional forecasting methods, which makes sense considering it should be used to identify the complex trends and improve the accuracy of day-ahead electric generation using photovoltaic panels. This article describes what the specific predicting means the stability of the grid, power trading and coherent renewable power and identifies the sense of the setting forth in practice of what has been proposed.