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Abstract
Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module) is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.
Highlights
Electrical load forecasting plays a pivotal role in electrical systems [1]
To evaluate the forecasting accuracy, several evaluation criteria are applied in this study, including average error (AE), mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE) and ζ I NDEX
The combined method can sufficiently incorporate the advantages of individual models; the application of linear combinations is limited because the possibility of nonlinear terms is ignored
Summary
Electrical load forecasting plays a pivotal role in electrical systems [1]. High-precision forecasting models can significantly improve power system management and provide effective information for economic operators [2]. If the forecasting error were to decrease by 1%, the operating costs would decrease by 10 million pounds [3]. Inaccurate forecasting results can result in huge losses for electric power companies. Overestimated forecasts lead to extra cost production, while underestimated forecasts lead to issues in supplying sufficient electricity, which could in turn result in large power system losses [4]. Many severe blackout events have occurred that have deeply affected social production and people’s lives. On 14 August 2003, the U.S.–Canada power grid suffered a serious blackout event. This accident affected approximately 50 million people and generated huge losses amounting to billions of dollars [5,6].
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