Abstract
Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning.
Highlights
Power load forecasting is the foundation and key task of management and control of power system [1]
We develop a method based on fuzzy clustering, which carries out interval forecasting on the basis of point forecasting
Most of the previous work focused on the deterministic point prediction of power load, seldom considered the other important aspect which is the interval prediction of power load, and this situation makes the prediction of power load not comprehensive
Summary
Power load forecasting is the foundation and key task of management and control of power system [1]. Xiaobo Zhang et al [25] successfully proposed a new power load prediction model, CS-SSA-SVM, which integrated singular spectrum analysis (SSA), support vector machine (SVM) and cuckoo search (CS) algorithm This model can significantly enhance the effectiveness of power load forecast. R. et al [27] proposed a new power load forecasting system by combining data preprocessing, hybrid optimized algorithm and certain individual conventional prediction methods, which conquers the shortcomings of individual conventional prediction model and obtains a single model optimization with higher prediction accuracy than traditional forecasting model. Another problem of power load forecasting is that the research direction is relatively single. Based on the hypothesis of distribution, this study develops a new architecture of interval prediction, which is better than most single model interval prediction architectures
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