Short term load forecast based on time series analysis: A case study

Short term load forecasting plays a vital role in the daily generation, efficient power system planning, unit maintenance, determining unit commitment and secured power system operation. There are number of approaches for short term load forecasting but it is observed that time series approach is most feasible and provides more reasonable accurate forecast. The present paper discuses the Autoregressive (AR) approach of time series analysis for short term load forecast for Tamilnadu (India) load data. The time series Autoregressive gives better forecasting results for 4 to 6 Hours ahead.

[1]  Suci Dwijayanti,et al.  Short Term Load Forecasting Using a Neural Network Based Time Series Approach , 2013, 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation.

[2]  Enjian Bai,et al.  Time-variant slide fuzzy time-series method for short-term load forecasting , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[3]  P. McSharry,et al.  Short-Term Load Forecasting Methods: An Evaluation Based on European Data , 2007, IEEE Transactions on Power Systems.

[4]  Panida Jirutitijaroen,et al.  Short-term load forecasting using time series analysis: A case study for Singapore , 2010, 2010 IEEE Conference on Cybernetics and Intelligent Systems.

[5]  G. Gross,et al.  Short-term load forecasting , 1987, Proceedings of the IEEE.

[6]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[7]  Shu-liang Liu,et al.  Power Load Forecasting Based on Neural Network and Time Series , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[8]  Nima Amjady,et al.  Short-term hourly load forecasting using time-series modeling with peak load estimation capability , 2001 .

[9]  Yunfang Xie,et al.  Short-Term Load Forecasting Based on the Method of Genetic Programming , 2007, 2007 International Conference on Mechatronics and Automation.