Evaluation of Short-Term Load Forecasting Techniques Applied for Smart Micro Grids

Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.

• Xiaolei Hu
• Enrico Ferrera
• Riccardo Tomasi
• Claudio Pastrone

• Short-term load forecasting
• smart micro grid
• linear regression
• artificial neural networks
• radial basis function network
• Gain.

[1] M. T. Hagan, S. M. Behr, "The time series approach to short term load
forecasting" IEEE Trans. Power Sys., vol. PWRS-2, no.3, Aug. 1987, pp.
785-791.
[2] Papalexopoulos, A. D., Hesterberg, T.C., "A regression-based approach
to short-term load forecasting" IEEE Trans. Power Sys., vol.5, no.4, Nov.
1990, pp 1535-1537.
[3] H. S. Hippert, C. E. Pedreira, and R. C. Souza, "Neural networks for
short-term load forecasting: A review and evaluation" IEEE Trans. Power
Sys., 16:44-55, 2001.
[4] J. W. Taylor, R. Buizza, "Neural network load forecasting with weather
ensemble predictions" IEEE Trans. Power Sys., 17:626-632, 2002.
[5] W. Charytoniuk, M. S. Chen, "Very short-term load forecasting using
aritificial neural networks" IEEE Trans Power Sys., 2000, pp15(1):
263-268.
[6] X. F. Li, E. Zou, T. S. Zhang, "Optimization design of feed-forward
neural network structure based on chaos wariables" Control and Decision,
2003, 18(6):703-707.
[7] G. Bugmann, "Normalized Gaussian radial basis function networks"
Neurocomputing (Special Issue on Radial Basis Function Networks), vol.
20, no.1, pp.97-110, 1998.
[8] G. B. Huang, P. Saratchandran, N. Sundararajan, "A generalized growing
and pruning RBF neural network for function approximation" IEEE Trans.
on Neural Networks, vol. 16, no.1, pp. 57-67, 2005.
[9] K. Z. Mao, G. B. Huang, "Neural slection for RBF neural network
classifier based on data structure preserving criterion" IEEE Trans. on
Neural Networks, vol. 16, no.6, pp. 1531-1540, 2005.
[10] Gontar Z., Hatziargyriou N., "Short-term load forecasting with radial
basis function network" IEEE Porto Power Tech Conference, vol. 3, Sep
2001.
[11] S. J. Kiartzis, A. G. Bakirtzis, "A fuzzy expert system for peak load
forecasting: application to the Greek Power System" Proceedings of the
10th Mediterranean Electrotechnical Conference, 3:1097-1100, 2000.
[12] Bretthauer G., Handschin E., Hoffmann W., "Expert systems application
to power systems--state-of-the-art and future trends" IFAC Symposium
on Control of Power Plants and Power Systems, Munich, Germany, 1992,
p.463-68.
[13] Adapa R., "Expert system applications in power system planning and
operations" IEEE Power Eng. Rev, 1994, 14(2):15-8.
[14] Liu C., Ma T., Liou K., Tsai M., "Practical use of expert systems in power
systems" Int J Eng Intelligent Sys. Electr. Eng. Commun 1994;
2(1):11-22.
[15] Bann J., Irisarri G., Kirschen D., Miller B., Mokhtari S., "Integration of
artificial intelligence applications in the EMS: issues and solutions" IEEE
Trans. Power Sys., 1996, pp11(1):475-82.
[16] Madan S., Bollinger K., "Applications of artificial intelligence in power
systems" Electric Power Sys. Res., 1997, pp 41:117-31.
[17] Barbato, A., Capone, A., Rodolfi, M., Tagliaferri D., "Forecating the
usage of household appliances through power meter sensors for demand
management in the smart grid," Smart Grid Communications, 2011 IEEE
International Conference on, vol., no., pp.404,409, 17-20 Oct. 2011.
[18] Fernandez, I., Borges, C.E., Penya, Y.K., "Efficient building load
forecasting," Emerging Technologies & Factoy Automation (ETFA),
2011 IEEE 16th Conference on, vol., no., pp.1,8,5-9 Sep. 2011.
[19] Berardino, J., Nwankpa, C.O., "Interval-specific building load forecasting
models for demand resource planning," Power System Technology
(POWERCON), 2012 IEEE International Conference on, vol., no., pp.1.5.
Oct. 30 2012-Nov. 2 2012.
[20] Canbing Li, Meiping Fu, Jincheng Shang, Peng Cheng, "A novel
probabilistic short-term load forecasting method for large power grid,"
Power and energy Engineering Conference (APPEEC), 2010 Asia-Pacific,
vol., no., pp.1,4,28-31 Mar. 2010.
[21] GreenCom project http://www.greencom-project.eu.
[22] Lean Y., Shouyang W., K. K. Lai, "An integrated data preparation scheme
for neural network data analysis" IEEE Tansactions on Knowledge and
Data Engineering, vol. 18, no.2, Feb. 2006.
[23] X. Hu, "DB-H reduction: a data preprocessing algorithm for data mining
applications" Applied Math. Letters, vol. 16, pp. 889-895, 2003
[24] M. Lou, "Preprocessing data for neural networks" Technical Analysis of
Stocks Commodities Magazine, Oct. 1993.
[25] GreenCom pilot, Island of Fur.
http://www.greencom-project.eu/island-of-fur.html.