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Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model
School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China
Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
* Authors to whom correspondence should be addressed.
Received: 21 August 2012; in revised form: 24 September 2012 / Accepted: 5 October 2012 / Published: 16 October 2012
Abstract: 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.
Keywords: power generation; joint parameter optimization; Harmony Search algorithm; combination forecasting method
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Cite This Article
MDPI and ACS Style
Sun, W.; Wang, J.; Chang, H. Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model. Energies 2012, 5, 3948-3971.
Sun W, Wang J, Chang H. Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model. Energies. 2012; 5(10):3948-3971.
Sun, Wei; Wang, Jingmin; Chang, Hong. 2012. "Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model." Energies 5, no. 10: 3948-3971.