Title Load Forecasting for building and residential electrical load demand

 

Currently the efficient use of households being interested research sujet in Vietnam not only is occupped 40% of electricity consumption, but also that is the new and potential research topic. There are some proposed solutions, among them smart metering system has been considered as an effective method for improving the pattern of power consumption of energy consumers [1] [2]. Smart metering system be used to collect data in house or building and a short-term electrical load forecasting problem can be solved. Load forecasting finds in use in sales, planning and manufacturing divisions of every industry. Literature review indicates the need to consider several factors such as time of a day, weather data and possible customer classes for effective one-step ahead and day ahead load forecasting on a feeder. There are some proposed models such as AR, ARMA, ARIMA, ARIMAX and Artificial Neural Network (ANN). The student need to analyse and evaluation of some short-term load forecasting techniques to get the best forecasting techniques to solve daily demand of residential load in take into account the weather variables (temperature, humidity) and seasons (hours, daily, seasons) variables.

 

Student prerequisites

This subject is dedicated to Vietnamese students as well as foreigner students at Master degree.

   
Supervisors/contacts

Dr. Nguyen Viet Tung: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

[1] Erol-Kantarci, Melike, and Hussein T. Mouftah. "Wireless sensor networks for smart grid applications. "Electronics, Communications and Photonics Conference (SIECPC), 2011 Saudi International. IEEE, 2011.

[2] Erol-Kantarci, Melike, and Hussein T. Mouftah. "The impact of smart grid residential energy management schemes on the carbon footprint of the household electricity consumption. "Electric Power and Energy Conference (EPEC), 2010 IEEE. IEEE, 2010.

[3] I. Moghram and S. Rahman, “Analysis and evaluation of five short-term load forecasting techniques,” IEEE Trans. Power Syst., vol. 4, pp. 1484-1491, Nov. 1989.

[4] Taylor, Eric Lynn, "Short-term Electrical Load Forecasting for an Institutional/Industrial Power System Using an Artificial NeuralNetwork. " Master's Thesis, University of Tennessee, 2013.

[5] Zheng, Jixuan, "Short-term Load Forecasting Using Neural Network For Future Smart Grid Application" (2014). Electronic Theses and Dissertations. Paper 735.

[6] Belvedere, B., et al. (2009). A microcontroller-based automatic scheduling system for residential microgrids. PowerTech, 2009 IEEE Bucharest, IEEE. [7] Ding, N., et al. (2011). Time series method for short-term load forecasting using smart metering in distribution systems. PowerTech, 2011 IEEE Trondheim, IEEE