Journal
ENERGY
Volume 35, Issue 4, Pages 1671-1678Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2009.12.015
Keywords
Load forecasting; Combined model; Adaptive particle swarm optimization; Forecasting accuracy
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Funding
- Ministry of Education overseas cooperation research Chunhui Projects in China [Z2007-1-62012]
- National Science Foundation of Gansu Province in China [Z5031-A25-010-G]
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Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising. (C) 2009 Elsevier Ltd. All rights reserved.
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