Application of adaptive neuro fuzzy inference system in demand forecasting for power engineering company

Golam Kabir and M. Ahsan Akhtar Hasin

International Journal of Industrial and Systems Engineering, 2014, vol. 18, issue 2, 237-255

Abstract: To enhance the commercial competitive advantage in a constantly fluctuating business environment, an organisation has to make the right decisions in time depending on demand information. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. Forecasting becomes a crucial process for manufacturing companies to effectively guiding several activities, and research has devoted particular attention to this issue. The objective of the paper is to propose a new forecasting mechanism which is modelled by adaptive neuro-fuzzy inference system (ANFIS) techniques to manage the fuzzy demand with incomplete information. ANFIS is utilised to harness the power of the fuzzy logic and artificial neural networks (ANN) through utilising the mathematical properties of ANNs in tuning rule-based fuzzy systems that approximate the way human's process information. To accredit the proposed model, it is implemented to forecast the demand of distribution transformer of a power engineering company of Bangladesh.

Keywords: adaptive neuro-fuzzy inference system; ANFIS; demand forecasting; distribution transformers; ANNs; artificial neural networks; fuzzy logic; power engineering; modelling; Bangladesh. (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:18:y:2014:i:2:p:237-255

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