Forecasting of daily load curve on monthly peak day using load research data and harmonics model

This work applies a harmonics model to forecast a daily load curve on a monthly peak day. The harmonics model is applied to capture periodic pattern of daily load and to avoid relying on weather-sensitive parameters. The harmonics model is a function of a base load, an hourly load, and a Fourier series. The dataset was obtained from load research data over the year 2008–2012 from the Provincial Electricity Authority of Thailand. It was found that the fifth harmonic model is proper to forecast the load curves on the monthly peak day. The forecasts of different tariff schedules are performed to compare the load patterns of residential, commercial, and industrial customers.

[1]  S. M. Al-Alawi,et al.  Principles of electricity demand forecasting. II. Applications , 1997 .

[2]  Y.H. Kareem,et al.  Monthly Peak-load Demand Forecasting for Sulaimany Governorate Using SARIMA. , 2006, 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America.

[3]  A. C. Liew,et al.  Short term load forecasting using genetic algorithm and neural networks , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[4]  Hong-Tzer Yang,et al.  Identification of ARMAX model for short term load forecasting: an evolutionary programming approach , 1995 .

[5]  W. R. Christiaanse Short-Term Load Forecasting Using General Exponential Smoothing , 1971 .

[6]  J. Nazarko,et al.  Estimating Substation Peaks from Load Research Data , 1997, IEEE Power Engineering Review.

[7]  E. H. Barakat,et al.  Forecasting monthly peak demand in fast growing electric utility using a composite multiregression-decomposition model , 1989 .

[8]  Zhao Yang Dong,et al.  Adaptive neural network short term load forecasting with wavelet decompositions , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[9]  N Amjady,et al.  Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique , 2011, IEEE Transactions on Power Systems.

[10]  Jia Zhengyuan Electricity consumption forecasting in peak load month based on variable weight combination forecasting model , 2008, 2008 IEEE International Conference on Automation and Logistics.

[11]  Saifur Rahman Formulation and analysis of a rule-based short-term load forecasting algorithm , 1990 .

[12]  Saleh M. Al-Alawi,et al.  Principles of electricity demand forecasting. I. Methodologies , 1996 .

[13]  Yogesh K. Bichpuriya,et al.  Non-parametric probability density forecast of an hourly peak load during a month , 2014, 2014 Power Systems Computation Conference.

[14]  A. K. Mahalanabis,et al.  Recursive short-term load-forecasting algorithm , 1974 .

[15]  Saifur Rahman,et al.  An expert system based algorithm for short term load forecast , 1988 .

[16]  H. K. Temraz,et al.  Analytic spatial electric load forecasting methods: A survey , 1992, Canadian Journal of Electrical and Computer Engineering.

[17]  Muhammad Asim Qayyum,et al.  SHORT-TERM PEAK DEMAND FORECASTING IN FAST DEVELOPING UTILITY WITH INHERIT DYNAMIC LOAD CHARACTERISTICS , 1990 .

[18]  M.E. El-Hawary,et al.  Application of least absolute value parameter estimation technique based on linear programming to short-term load forecasting , 1996, Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering.

[19]  Furong Li,et al.  Analysis of the relationship between load profile and weather condition , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[20]  M. Beiraghi,et al.  Discrete Fourier Transform Based Approach to Forecast Monthly Peak Load , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[21]  Saifur Rahman,et al.  Fuzzy neural networks for time-series forecasting of electric load , 1995 .

[22]  Saifur Rahman,et al.  Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.

[23]  Magdy M. A. Salama,et al.  Application of the decomposition technique for forecasting the load of a large electric power network , 1996 .

[24]  N. H. Skinner Load research and its application to electricity demand forecasting , 1984 .