Abstract
A study on load forecasting prediction is important for efficient management of users' demands for any utility such as electricity and gas. Many computational intelligence approaches have been adopted for short term electricity load forecasts. For instance, artificial neural network, traditional fuzzy neural networks, support vector machines and other intelligent methods have been adopted in the literature. It is well known that load forecast is an important input for management decision systems in every power supply organization. Fuzzy logic systems (type-1 and type-2) have proven to be one of the effective tools for many load forecasting problems. In this study, interval type-2 intuitionistic fuzzy logic system equipped with membership functions, non-membership functions and hesitation indices is proposed for very short and short-term load forecasting for the first time. Gradient descent method is used to optimize the parameters of the developed model. The proposed model is compared with the traditional interval type-2 fuzzy logic and traditional neural network systems. Experimental analyses reveal that the proposed model outperform the two traditional systems in many load instances.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advances in Scientific Research and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
