Residential Electricity Demand Forecasting using Data Mining

Residential Electricity Demand Forecasting using Data Mining

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-49 Number-1
Year of Publication : 2017
Authors : Ms. Seema Kore, Prof. V. S. Khandekar
DOI :  10.14445/22315381/IJETT-V49P204

Citation 

Ms. Seema Kore, Prof. V. S. Khandekar "Residential Electricity Demand Forecasting using Data Mining", International Journal of Engineering Trends and Technology (IJETT), V49(1),27-32 July 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
In this paper, the proposed system is designed which predicts the electricity demand. Data mining techniques are used such as data cleaning, data smoothing to get the data required for prediction.The Artificial Neural Network (ANN) plays a great role in forecasting the electricity consumption. The existing methodology used to find the electricity consumption and demand prediction for household used ANN, data mining and data preprocessing. The context features like weather, temperature, humidity and public holiday are used as input for the prediction system. Along with context features seasonwise electricity consumption forecasting to achieve improved accuracy is done using proposed system which is based on Support Vector Regression (SVR) and Linear Regression (LR). LR and SVR gives better accuracy than the existing system. LR produces the MAPE value of 0.59% and SVR produces MAPE value of 0.11%. The RMSE (Root Mean Squared Error) performance metrics is used to evaluate the system performance. The RMSE value for LR is 0.73 and for SVR it is 0.34.

 References

[1] Yu-Hsiang Hsiao, “Household Electricity Demand Forecast Based on Context Information and User Daily Schedule Analysis From Meter Data”, IEEE Transactions on Industrial Informatics, Vol. 11, No. 1, February,2015.
[2] Navjot Kaur and Amrit Kaur, “Predictive Modelling Approach to Data Mining for forecasting electricity consumption ”, 6th International Conference-Cloud System and Big Data Engineering(Confluence),2016.
[3] Harmanjot Singh Sandhu, Liping Fang and Ling Guan, “Forecasting Day-Ahead Electricity Prices using Data Mining and Neural Network Techniques”,2014.
[4] Weijie Mai and C. Y. Chung, “Electric Load Forecasting for Large Office Building Based on Radial Basis Function Neural Network”, 2014.
[5] Jian Deng, “Modelling and Prediction of China’s Electricity Consumption using Artificial Neural Network”, 6th International Conference on Natural Computation,2010.
[6] Hiroyuki Mori and Akira Awata, “Data Mining of Electricity Price Forecasting with Regression Tree and Normalized Radial Basis Function Network”, IEEE, 2007.
[7] Noorollah Karimtabar and Sadegh Pasban, “Analysis and predicting electricity energy consumption using data mining techniques- A case study I.R. Iran - Mazandaran province”, 2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA 2015) March 11-12, 2015.
[8] Amit Jain, “Clustering based Short Term Load Forecasting using Support Vector Machines”, IEEE Bucharest Power Tech Conference, 2009.
[9] Hoda K. Mohamed, “Data Mining for Electrical Load Forecasting In Egyptian Electrical Network”, IEEE, 2006.
[10] Jongwoo Choi and Youn Kwae Jeong, “Development of the Simple Building Electric Power Prediction Model with Local Weather Forecast based on Clustering and Silhouette Algorithm”, IEEE, 2015.
[11] Vincent Thouvenot and Audrey Pichavant, “Electricity Forecasting Using Multi-Stage Estimators of Nonlinear Additive Models”, IEEE Transactions on Power Systems,VOL.31,NO.5,September 2016.
[12] Ali Darudi and Masoud Bashari, “Electricity Price Forecasting using A New Data Fusion Algorithm”, The Institution of Engineering and Technology Generation, Transmission, Distribution, Vol.9, 2015.
[13] Lei Wu and Mohammad Shahidehpour, “A Hybrid Model for Integrated Day-Ahead Electricity Price and Load Forecasting in Smart Grid”, The Institution of Engineering and Technology Generation, Transmission, Distribution,, Vol.8, 2014.

Keywords
Predictive modeling, Data mining, Artificial Neural Network, Context Features, Support Vector Regression, Linear Regression.