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A novel composite electricity demand forecasting framework by data processing and optimized support vector machine

Ping Jiang, Ranran Li, Ningning Liu and Yuyang Gao

Applied Energy, 2020, vol. 260, issue C, No S0306261919319300

Abstract: Reliable forecast of electricity can encourage accessible and responsible information for scholars, policymakers, end-consumers and managers of the electricity market. Numerous electricity forecasting methods have been achieved commendably, the performance of which varies depending on numerical characteristics and operational conditions. In this study, the composite forecasting concept is introduced and implemented to show the potential of forecasting performance. This modeling concept is a remarkable ability to identify and measure any seasonal relationship that exists in electricity demand data. Moreover, it is available as a toolbox in many of the programming operation research. In the module of nonlinear time series decomposition, the noise disturbance is initially considered before extracting the seasonal variation to support the condition that the linear and stationary time series should be used for the seasonality identifying method. Also, we further provide a new insight of prediction intervals estimation to better reflect the uncertainties of the underlying challenging power system plan and operation. The results show that the proposed model can generate promising forecasts compared to the other combination schemata and it can be useful for both policy-makers and public agencies to guarantee the security and regulation of the power system.

Keywords: Electricity demand forecasting; Fast fourier transform; Seasonal time series; Optimization algorithm; Support vector machine (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.apenergy.2019.114243

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