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Institutional Electricity Load Forecasting using Classical and Intelligent Forecasting Techniques

IR@CEERI: CSIR-Central Electronics Engineering Research Institute, Pilani


Field	Value

Title	Institutional Electricity Load Forecasting using Classical and Intelligent Forecasting Techniques

Creator	Patel, SS Singh, A Kumar, N Panchariya, PC Akbar, SA

Subject	Digital Systems

Description	An accurate forecasting of Institutional Electricity load can proved to be useful asset for efficient utilization of the infrastructure available in terms of future demand and supply. Time series method of forecasting has got very wide applications like salts forecasting, yield prediction and Supply Chain Monitoring (SCM) system etc. This paper presents a classical time series models available for predicting the future demand. The classical models used to predict the future load and demand assumes the linear relationship between input and output but in the real world this doesn't seems to be practical. The intelligent and self-learning models like neural network has the lead to approximate any kind of non-linear function and can fit into these situations. Classification and prediction capabilities of Neural Network has also shown a great potential in forecasting. A neural network based time series forecasting model is also developed for electricity load forecasting. Behavioral pattern and trend of the experimental data are being studied and analyzed for accurate forecasting of electricity load.

Date	2018

Type	Conference or Workshop Item PeerReviewed

Format	application/pdf

Identifier	http://ceeri.csircentral.net/595/1/27-2018.pdf Patel, SS and Singh, A and Kumar, N and Panchariya, PC and Akbar, SA (2018) Institutional Electricity Load Forecasting using Classical and Intelligent Forecasting Techniques. In: 15th IEEE India Council International Conference (INDICON-2018), December 15-18, 2018, Amrita Vishwa Vidyapeetham, Combatore.

Relation	http://ceeri.csircentral.net/595/