Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods

Applications such as generator scheduling, household smart device scheduling, transmission line overload management and microgrid islanding autonomy all play key roles in the smart grid ecosystem. Management of these applications could benefit from short-term load prediction, which has been successfully achieved on large-scale systems such as national grids. However, the scale of the data for analysis is much smaller, similar to the load of a single transformer, making prediction difficult. This paper examines several prediction approaches for day and week ahead electrical load of a community of houses that are supplied by a common residential transformer, in particular: artificial neural networks; fuzzy logic; auto-regression; auto-regressive moving average; auto-regressive integrated moving average; and wavelet neural networks. In our evaluation, the methods use pre-recorded electrical load data with added weather information. Data is recorded from a smart-meter trial that took place during 2009-2010 in Ireland, which registered individual household consumption for 17 months. Two different scenarios are investigated, one with 90 houses, and another with 230 houses. Results for the two scenarios are compared and the performances of the evaluated prediction methods are discussed.

[1]  P. Whittle.  Hypothesis testing in time series analysis , 1954 .

[2]  Xiao-Jun Zeng,et al.  Short-Term and Midterm Load Forecasting Using a Bilevel Optimization Model , 2009, IEEE Transactions on Power Systems.

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

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

[5]  N. Amjady,et al.  Short-Term Bus Load Forecasting of Power Systems by a New Hybrid Method , 2007, IEEE Transactions on Power Systems.

[6]  Alfredo Núñez,et al.  Load profile generator and load forecasting for a renewable based microgrid using Self Organizing Maps and neural networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[7]  Jorge J. Gómez-Sanz,et al.  A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants , 2013, IEEE Communications Magazine.

[8]  Zhou Quan,et al.  RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment , 2008, IEEE Transactions on Power Systems.

[9]  Tamás D. Gedeon,et al.  Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm , 1998, IEEE Trans. Neural Networks.

[10]  V. Lo Brano,et al.  Forecasting daily urban electric load profiles using artificial neural networks , 2004 .

[11]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[12]  Yung-Ruei Chang,et al.  Short-term load forecasting via fuzzy neural network with varied learning rates , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[13]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[14]  Patrick P. K. Chan,et al.  Multiple classifier system for short term load forecast of Microgrid , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[15]  Siobhán Clarke,et al.  Analysis of Approaches to Coordinated Charging of Electric Vehicles on the Distribution Grid , 2013, International Conference on Smart Grids and Green IT Systems.

[16]  Dominic Maratukulam,et al.  ANNSTLF-a neural-network-based electric load forecasting system , 1997, IEEE Trans. Neural Networks.

[17]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[19]  Jorge J. Gómez-Sanz,et al.  A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework , 2012, Sensors.

[20]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[21]  Hamidreza Zareipour,et al.  Electricity Price and Demand Forecasting in Smart Grids , 2012, IEEE Transactions on Smart Grid.

[22]  Hesham K. Alfares,et al.  Electric load forecasting: Literature survey and classification of methods , 2002, Int. J. Syst. Sci..

[23]  Farshid Keynia,et al.  Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy , 2010, IEEE Transactions on Smart Grid.

[24]  M. F. Irzaq bin Khamis,et al.  Electricity forecasting for small scale power system using fuzzy logic , 2010, 2010 Conference Proceedings IPEC.

[25]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.