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Research Article
 

Application of a Hybrid Algorism Combining Fuzzy Theory and Neural Network for Heating Load Forecasting



Wang Meiping, Tian Qi, Zhang Jiao and Jin Nana
 
ABSTRACT

Accurate load forecasting has a great significance for heating companies to make the best decisions in terms of unit commitment, generation and maintenance planning, etc. It is necessary that heating generation companies have prior knowledge of future demand with great accuracy. A flexible algorithm based on Fuzzy Logic (FL) and artificial neural network (ANN) is presented to cope with optimum heating load forecasting in noisy, uncertain and complex environments. It is concluded that the selected newly method considerably outperform the WNN models in terms of Mean Absolute Percentage Error (MAPE). The proposed flexible FL-ANN algorithm can be easily applied to complex, non-linear and uncertain datasets, such as central heating system.

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  How to cite this article:

Wang Meiping, Tian Qi, Zhang Jiao and Jin Nana, 2013. Application of a Hybrid Algorism Combining Fuzzy Theory and Neural Network for Heating Load Forecasting. Journal of Applied Sciences, 13: 1911-1915.

DOI: 10.3923/jas.2013.1911.1915

URL: https://scialert.net/abstract/?doi=jas.2013.1911.1915

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