A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks and Auto-Regressive Moving Average

Abstract

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

“…Researchers have developed different methods for solar irradiance forecasting, such as statistical approaches using historical data, use of numerical weather prediction (NWP) models, tracking cloud movements from satellite images and tracking cloud movements from direct ground observations using sky cameras [8]. Some reviews on renewable energy forecasting models are given in References [6][7][8][9][10][11] among others. Some notable methods are discussed in the following paragraphs.…”

Solar Power System Planning and Design

“…Machine learning methods were successfully applied in PV output prediction models. Xie et al [14] proposed a hybrid short-term forecasting method based on the variational mode decomposition (VMD) technique, the deep belief network (DBN), and the auto-regressive moving average (ARMA) model to improve forecasting accuracy. The results showed that the hybrid forecasting method offers better accuracy and stability than the single prediction methods.…”

A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

“…Step 4: Calculate w according to Equation (17) and update the individual position according to Equations (13) and (16).…”

“…It consists of multiple restricted Boltzmann machines (RBMs) and a back propagation (BP) neural network, allowing processing large amounts of data. In recent decades, the DBN has been successfully applied to fault diagnosis, wind speed forecasting, breast cancer classification, and so on [11][12][13][14][15][16]. Due to its advantage in prediction accuracy, in this paper, we introduce the DBN to conduct the PM2.5 concentration prediction.…”