A Hybrid Load Forecasting Method Based on Neural Network in Smart Grid

Abstract

Power load forecasting is of great significance to ensure the smooth operation of smart grid. Because the load generation and consumption are related to the grid internal and environmental factors external, reliable and accurate power load forecasting is undoubtedly challenging in smart grid. Since weather factors are always the leading causes that affecting power generation load in smart grid, especially in distributed photovoltaic power generation, we propose a load forecasting method to realize the forecast of the generated load under different weather conditions in this paper. We firstly investigates the combined effect of various weather factors on power load comprehensively. Specially, the parametric regression models are utilized to analyse the relationship between the power load and weather factors. Secondly, a hybrid forecasting method based on Multilayer Perceptron (MLP) neural network is proposed to achieve reliable and accurate power load forecasting of various weather conditions. Different from the existing works, we not only take into account the weather factors, but also select corresponding parametric models integrated as the additional input of the MLP neural network to predict the power load. More importantly, a modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced to train the formulated model. As a result, the training process of the neutral network can be accelerated in the sense that iteration times are reduced, in which case the learning accuracy can also be guaranteed. The proposed method is evaluated on the real dataset which consist of meteorological factors and corresponding load data. The results show the proposed method outperforms the existing algorithms in prediction accuracy. The prediction error Mean Square Error(MSE) and Root Mean Squared Error(RMSE) can be reduced by 36.28% and 20.18% respectively, which ensure the reliability of the power load forecasting.