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Prediction of Electricity Power Consumption using ANN

Rungta International Journal of Electrical and Electronics Engineering

Volume 1 Issue 1

Published: 2016
Author(s) Name: Shruti Mishra, Lakhwinder Kaur | Author(s) Affiliation: RCET, Bhilai, Chhattisgarh, India
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Forecasting of electricity consumption is considered as one of the most significant aspect of effective management of power system. On a long term basis, it allows decision makers of a power generating company to decide when to build new power plants, transmission and distribution networks. On a short term basis, it can be used to allocate resources in a power grid to supply the demand continuously. Forecasting is basically divided into three categories: short-term, medium-term, and long-term. Short-term refers to an hour to a week forecast, while medium-term refers to week to year, and predictions that run more than a year refers to long-term. In this thesis, we forecast electricity consumption on a short-term basis for a particular region in Norway using a relatively novel approach: Artificial Neural Network and Multiple Regression Method. We design the best feature vector suitable for forecasting electricity consumption using various factors such as previous consumption, temperature, days of the week and hour of the day.

Keywords: Artificial Neural Network, Data Mining, Prediction, Mean Square Error

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