论文信息 - Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models

Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models

—The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly, monthly, or quarterly. The time series data in our study was individual household electric power The result of the study showed that the ARIMA model was the best model for finding the most suitable forecasting period in monthly and quarterly. On the other hand, ARMA model was the best model for finding the most suitable forecasting period in daily and weekly. Then, we calculated the most suitable forecasting period and it showed that the method were suitable for short term as 28 days, 5 weeks, 6 months and 2 quarters, respectively.

Pasapitch Chujai | Nittaya Kerdprasop | Kittisak Kerdprasop

[1] George E. P. Box,et al. Time Series Analysis: Forecasting and Control , 1977 .

[2] D. J. Newman,et al. UCI Repository of Machine Learning Database , 1998 .

[3] Samer S. Saab,et al. Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon , 2001 .

[4] J. Contreras,et al. ARIMA Models to Predict Next-Day Electricity Prices , 2002, IEEE Power Engineering Review.

[5] Anju Vyas. Print , 2003 .

[6] B. Bowerman,et al. Forecasting, time series, and regression : an applied approach , 2005 .

[7] A.J. Conejo,et al. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models , 2005, IEEE Transactions on Power Systems.

[8] Oscar Castillo,et al. Proceedings of the International MultiConference of Engineers and Computer Scientists 2007, IMECS 2007, March 21-23, 2007, Hong Kong, China , 2007, IMECS.

[9] V. Ediger,et al. ARIMA forecasting of primary energy demand by fuel in Turkey , 2007 .

[10] Kung-Sik Chan,et al. Time Series Analysis: With Applications in R , 2010 .

[11] Qing Zhu,et al. Household Energy Consumption in China: Forecasting with BVAR Model Up to 2015 , 2012, 2012 Fifth International Joint Conference on Computational Sciences and Optimization.

[12] L. Pazvakawambwa,et al. Forecasting methods and applications. , 2013 .

[13] Robert H. Shumway,et al. Time series analysis and its applications : with R examples , 2017 .