Forecasting Annual Power Generation Using a Harmony Search Algorithm-Based Joint Parameters Optimization Combination Model
Wei Sun,
Jingmin Wang and
Hong Chang
Additional contact information
Wei Sun: School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China
Jingmin Wang: School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China
Hong Chang: Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
Energies, 2012, vol. 5, issue 10, 1-24
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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.mdpi.com/1996-1073/5/10/3948/pdf (application/pdf)
https://www.mdpi.com/1996-1073/5/10/3948/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:5:y:2012:i:10:p:3948-3971:d:20674
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager (indexing@mdpi.com).