An Accurate Hybrid Approach for Electric Short-Term Load Forecasting

Abstract

For efficient working of the power system, an accurate approach for short-term load forecasting (STLF) is suggested. To improve the accuracy of forecasting, various weather conditions, such as temperature, humidity, dew point, wind chill, and wind speed, are considered and their impact on the accuracy of load forecasting is studied in detail in terms of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Maximum Error (ME) errors. The proposed hybrid approach consists of Support Vector Regression (SVR) and fuzzy because SVR can forecast the ability of small dataset and fuzzy system to handle non-linear weather conditions and uncertainty of load in forecasting. For load forecasting, time of the day, historical load i.e. previous one-month hourly load, weather conditions, calendar days for the last 10 days, sunny time, temperature at the same time in previous day, and average temperature of last three hours are taken into account. The proposed approach provides accurate load forecasting for a day regardless of its being a working day or holiday, while fewer days are used for load prediction viz. previous one month, while no special care is taken for weekend. The suggested approach is tested on standard electricity datasets: EUNITE network 1997 and New England of America of 2012 and 2019. Simulation results show better effectiveness and the superiority of the proposed approach when compared with other existing methods for daily load forecasting viz. ANN, Bayesian, and Least Square Support Vector Machine, etc.

KEYWORDS:

• EUNITE
• Fuzzy prediction
• Kernel Trick
• New England dataset
• STLF
• SVR

Additional information

Notes on contributors

Alireza Sina

Alireza Sina received the BEng degree from Dezful University, and the MSc degree from SCU of Ahvaz in 1999 and 2004, respectively. From 2000 to 2005, he was working as the production manager of the Iranian Gas Company in Tehran. Currently, he is a faculty member of ACECR in Ahvaz (since 2005) and research scholar from UIET, PU Chandigarh India. His research interests include intelligent control and optimization of power systems. E-mail: [email protected]

Damanjeet Kaur

Damanjeet Kaur received her BEng degree in electrical engineering from Panjab University in 1997. She received MTech from PTU Jalandhar in 2000 and PhD from IIT, Roorkee India in 2008. She has been a faculty member in Panjab University, Chandigarh India since 2006. Her current areas of interest are distribution system planning and power system optimization.