Middle-long power load forecasting based on particle swarm optimization. (English) Zbl 1186.68382
Summary: Middle-long forecasting of electric power load is crucial to electric investment, which is the guarantee of the healthy development of electric industry. In this paper, the particle swarm optimization (PSO) is used as a training algorithm to obtain the weights of the single forecasting method to form the combined forecasting method. Firstly, several forecasting methods are used to do middle-long power load forecasting. Then the upper forecasting methods are measured by several indices and the entropy method is used to form a comprehensive forecasting method evaluation index, following which the PSO is used to attain a combined forecasting method (PSOCF) with the best objective function value. We then obtain the final result by adding all the results of every single forecasting method. Taking actual load data of a power grid company in North China as a sample, the results show that PSOCF model improves the forecasting precision compared to the traditional models.
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 90B15 | Stochastic network models in operations research |
| 90C59 | Approximation methods and heuristics in mathematical programming |
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