Neural network fusion optimization for photovoltaic power forecasting

Abstract

This paper aims to establish a comprehensive photovoltaic power generation prediction model. By collecting photovoltaic power generation data and weather data for a year, we analyzed the photovoltaic output characteristics in different seasons and found that the output characteristics in different seasons are also different. This article uses three neural network models, Long Short Term Memory Network, Recurrent Neural Network, and Dense Neural Network, to analyze the output characteristics of different seasons. Training, prediction, and prediction error analysis found that different models have different prediction accuracy in different seasons. Therefore, this paper proposes a weighted ensemble model add weights model based on the Nelder-Mead method to train and predict different seasons respectively. By analyzing the prediction error, the prediction accuracy needs to be better than a single model. We add noise to the data set to simulate unstable lighting conditions such as rainy days, and train and predict the data set after adding noise. The prediction results show that the comprehensive model has higher prediction accuracy than a single model in extreme weather. In order to verify the reliability of the model, this article uses a sliding window to extract the confidence interval of the prediction results, and uses the Bootstrap method to calculate the confidence interval. By analyzing and comparing each model’s Average Coverage, Root Mean Squared Length, and Mean Width, the prediction accuracy and reliability of add weights model are better than those of a single model.