Short-term forecasting of residential building load for distributed energy management

It is expected that energy management systems (EMS) on the demand side can be used as a method for enhancing the capability of balancing supply and demand of a power system under the anticipated increase of renewable energy generation such as photovoltaics (PV). Energy demand and solar radiation must be predicted in order to realize the optimal operation scheduling of demand side appliances by EMS, including heat pump water heaters, PV systems, and solar powered water heaters. This paper presents a day-ahead forecasting method for electricity consumption in a house to contribute to energy management. Ten forecasting methods are examined using real survey data from 35 households over a year in order to verify forecast accuracy. A daily battery operation model is also developed to evaluate the effect of load forecasts.

[1]  Jesús M. Zamarreño,et al.  Prediction of hourly energy consumption in buildings based on a feedback artificial neural network , 2005 .

[2]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[3]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[4]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[5]  Antonio Messineo,et al.  Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption , 2012 .

[6]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[7]  E. Gonzalez-Romera,et al.  Monthly Electric Energy Demand Forecasting Based on Trend Extraction , 2006, IEEE Transactions on Power Systems.

[8]  M. Etezadi-Amoli,et al.  Smart meter based short-term load forecasting for residential customers , 2011, 2011 North American Power Symposium.

[9]  Shinji Wakao,et al.  Operation design of PV system with storage battery by using next-day residential load forecast , 2011, 2011 37th IEEE Photovoltaic Specialists Conference.

[10]  Spyros G. Tzafestas,et al.  Computational Intelligence Techniques for Short-Term Electric Load Forecasting , 2001, J. Intell. Robotic Syst..

[11]  Takashi Ikegami,et al.  Optimum operation scheduling model of domestic electric appliances for balancing power supply and demand , 2010, 2010 International Conference on Power System Technology.

[12]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[13]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[14]  Fredrik Wallin,et al.  Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting , 2012 .

[15]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[16]  K. Ogimoto,et al.  Estimation of appliance electricity consumption by monitoring currents on residential distribution boards , 2010, 2010 International Conference on Power System Technology.

[17]  Ryuichi Yokoyama,et al.  Load forecasting on demand side by multi-regression model for operation of battery energy storage system , 2009, 2009 44th International Universities Power Engineering Conference (UPEC).

[18]  Yoseba K. Penya,et al.  Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings , 2013 .

[19]  Marios M. Polycarpou,et al.  Short Term Electric Load Forecasting: A Tutorial , 2007, Trends in Neural Computation.