Journal
ENERGY
Volume 118, Issue -, Pages 473-480Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2016.10.003
Keywords
Grey prediction model; Particle swarm optimization; Initial value; Electricity consumption
Categories
Funding
- National Natural Science Foundation of China [71302178, 71071077]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Talent Introduction Project of Nanjing Audit University
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Forecasting of electricity energy consumption (EEC) has been always playing a vital role in China's power system management, and requires promising prediction techniques. This paper proposed an optimized hybrid GM(1,1) model to improve prediction accuracy of EEC in short term. GM(1,1) model, in spite of successful employing in various fields, sometimes gives rise to inaccurate solution in practical applications. Time response function (TRF) is an important factor deeply influencing modeling precision. Aiming to enhance forecasting performance, this paper proposed a novel grey model with optimal time response function, referred to as IRGM(1,1) model. As of unknown variables in TRF, a nonlinear optimization method, based on particle swarm algorithm, is constructed to obtain optimal values, for shrinking simulation errors and improving adaptability to characteristics of raw data. The forecasting performance has been confirmed by electricity consumption data of China, comparing with three alternative grey models. Application demonstrates that the proposed method can significantly promote modeling accuracy.(C) 2016 Elsevier Ltd. All rights reserved.
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