A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm

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• 作者：Rui Hu^a ; ^bAuthor Vitae ; Shiping Wen^a ; ^b ; ^{wenshiping226@126.com}Author Vitae ; Zhigang Zeng^a ; ^bAuthor Vitae ; Tingwen Huang^cAuthor Vitae
• 关键词：Short term power load forecasting ; Generalized regression neural network ; Fruit fly optimization algorithm ; Decreasing step size
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：19 January 2017
• 年：2017
• 卷：221
• 期：Complete
• 页码：24-31
• 全文大小：923 K
• 卷排序：221

文摘

Short term power load forecasting plays an important role in the security of power system. In the past few years, application of artificial neural network (ANN) for short-term load forecasting (STLF) has become a research hotspots. Generalized regression neural network (GRNN) has been proved to be suitable for solving the non-linear problems. And according to the historical load curve, it can be known that STLF is a non-linear problem. Thus, the GRNN was used for STLF in this paper. However, the value of spread parameter σ determines the performance of the GRNN. The fruit fly optimization algorithm with decreasing step size (SFOA) is introduced to select an appropriate spread parameter σ. Combined with the weather factors and the periodicity of short-term load, an effective STLF model based on the GRNN with decreasing step FOA was proposed. Performance of the proposed SFOA-GRNN model is compared with other ANN on the basis of prediction error.