文献类型：期刊文献

英文题名：Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model

作者：Sun, Wei[1];Wang, Jingmin[1];Chang, Hong[2]

机构：[1]N China Elect Power Univ, Sch Econ & Management, Baoding 071003, Hebei, Peoples R China;[2]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2012

卷号：5

期号：10

起止页码：3948

外文期刊名：ENERGIES

收录：;EI(收录号：20124915760111);WOS:【SCI-EXPANDED(收录号:WOS:000310563000014)】；

基金：This work was supported by "the Fundamental Research Funds for the Central Universities (12MS137)" and "the National Natural Science Foundation of China (NSFC) (71071052)".

语种：英文

外文关键词：power generation; joint parameter optimization; Harmony Search algorithm; combination forecasting method

摘要：Accurate power generation forecasting provides the basis of decision making for electric power industry development plans, energy conservation and environmental protection. Since the power generation time series are rarely purely linear or nonlinear, no single forecasting model can identify the true data trends exactly in all situations. To combine forecasts from different models can reduce the model selection risk and effectively improve accuracy. In this paper, we propose a novel technique called the Harmony Search (HS) algorithm-based joint parameters optimization combination model. In this model, the single forecasting model adopts power function form with unfixed exponential parameters. The exponential parameters of the single model and the combination weights are called joint parameters which are optimized by the HS algorithm by optimizing the objective function. Real power generation time series data sets of China, Japan, Russian Federation and India were used as samples to examine the forecasting accuracy of the presented model. The forecasting performance was compared with four single models and four combination models, respectively. The MAPE of our presented model is the lowest, which shows that the proposed model outperforms other comparative ones. Especially, the proposed combination model could better fit significant turning points of power generation time series. We can conclude that the proposed model can obviously improve forecasting accuracy and it can treat nonlinear time series with fluctuations better than other single models or combination models.