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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Hybrid Short-term Load Forecasting Using Principal Component Analysis and MEA-Elman Network
verfasst von : Guangqing Bao, Qilin Lin, Dunwei Gong, Huixing Shao
Erschienen in: Intelligent Computing Methodologies
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Abstract
Meteorological factors, the main causes that impact the power load, have become a research focus on load forecasting in recent years. In order to represent the influence of weather factors on the power load comprehensively and succinctly, this paper uses PCA to reduce the dimension of multi-weather factors and get comprehensive variables. Besides, in view of a relatively low dynamic performance of BP network, a model for short-term load forecasting based on Elman network is presented. When adopting the BP algorithm, Elman network has such problems as being apt to fall into local optima, many iterations and low efficiency. To overcome these drawbacks, this paper improves the active function, optimizes its weights and thresholds using MEA, and formulates a MEA-Elman model to forecast the power load. An example of load forecasting is provided, and the results indicate that the proposed method can improve the accuracy and the efficiency.