Short-term output power forecasting of photovoltaic systems based on the deep belief net

Abstract

Photovoltaic power is now a major green energy resource, and its generated power can be directly connected to the power grid. However, the stability of power grid may be affected by the random and intermittent characteristics of photovoltaic power. In order to solve this problem, a forecasting model based on the deep belief nets is proposed. First, affecting factors of photovoltaic power generation are studied, including solar radiation intensity, air temperature, relative humidity, and wind speed. Based on the correlation coefficient between output power and each factor, the most influential factors can be determined and used as inputs of the proposed forecasting model for training process. Second, the forecasting model is then established and applied to predict the photovoltaic output powers for 2 weeks in summer and winter, respectively. The mean absolute percentage error, mean squared error, and Theil's inequality coefficient are used to evaluate the performance efficiency between the proposed deep belief net model and back propagation neural network model. The performance outcomes reveal that the proposed deep belief net model can improve the prediction errors with rapid convergence significantly, better than the back propagation model.

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

“…A classification can be made based on how the weather data are utilized as input. Three categories were observed, studies that only use weather forecast [5][6][7][8][9][10][11][12][13][14][15][16][17][18], those that use only weather observation [19][20][21][22][23][24][25], and those that use both forms of weather data [1,[26][27][28][29][30]. In the first category, planning and projection is required before the actual generation of solar energy, but they are highly correlated with the errors that meteorological stations can make in the forecasting.…”

Application of Multi-Species Differential Evolution Algorithm in Sustainable Microgrid Model

“…The output power of the PV is affected by factors such as temperature and light radiation degree [23][24][25], and its equation can be briefly written as [22]:…”

A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning

“…In the case of using current weather as predictors, an implied hypothesis is that future irradiance and PV generation are related to the current weather. Studies in this stream adopt methods, such as neural networks [7], heterogeneous regressions [8], and deep belief network [9]. When the time span of recorded weather observations is expanded, time-series analysis approaches are adopted, such as autoregressive moving average (ARMA) [10], autoregressive integrated moving average (ARIMA) [11][12][13], and a few variants of recurrent neural networks (RNNs) [14,15].…”