Abstract
The scheduling of household smart load devices play a key role in microgrid ecosystems, and particularly in underpowered grids. The management and sustainability of these microgrids could benefit from the application of short-term prediction for the energy production and demand, which have been successfully applied and matured in larger scale systems, namely national power grids. However, the dynamic change of energy demand, due to the necessary adjustments aiming to render the microgrid self-sustainability, makes the forecasting process harder. This paper analyses some prediction techniques to be embedded in intelligent and distributed agents responsible to manage electrical microgrids, and especially increase their self-sustainability. These prediction techniques are implemented in R language and compared according to different prediction and historical data horizons. The experimental results shows that none is the optimal solution for all criteria, but allow to identify the best prediction techniques for each scenario and time scope.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Colson, C., Nehrir, M.: A review of challenges to real-time power management of microgrids. In: 2009 IEEE Power & Energy Society General Meeting, pp. 1–8. IEEE, July 2009
Menasche, D.S., Rocha, A.A.A., e Silva, E.A.d.S., Leao, R.M., Towsley, D., Venkataramani, A.: Estimating self-sustainability in peer-to-peer swarming systems (2010)
Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, New York (2002). ISBN 978-0471496915
Leitao, P., Vrba, P., Strasser, T.: Multi-agent systems as automation platform for intelligent energy systems. In: IECON 2013–39th Annual Conference of the IEEE Industrial Electronics Society, pp. 66–71. IEEE, November 2013
McArthur, S.D.J., Davidson, E.M., Catterson, V.M., Dimeas, A.L., Hatziargyriou, N.D., Ponci, F., Funabashi, T.: Multi-agent systems for power engineering applications part I: concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)
Ferreira, A., Leitão, P., Barata Oliveira, J.: Formal specification of a self-sustainable holonic system for smart electrical micro-grids. In: Borangiu, T., Trentesaux, D., Thomas, A., Leitão, P., Barata Oliveira, J. (eds.) Service Orientation in Holonic and Multi-Agent Manufacturing. SCI, vol. 694, pp. 179–190. Springer, Cham (2017). doi:10.1007/978-3-319-51100-9_16
Feinberg, E.A., Genethliou, D.: Load forecasting. In: Chow, J.H., Wu, F.F., Momoh, J. (eds.) Applied Mathematics for Restructured Electric Power Systems, pp. 269–285. Springer, Boston (2006)
Hahn, H., Meyer-Nieberg, S., Pickl, S.: Electric load forecasting methods: tools for decision making. Eur. J. Oper. Res. 199(3), 902–907 (2009)
Kyriakides, E., Polycarpou, M.: Short term electric load forecasting: a tutorial. In: Chen, K., Wang, L. (eds.) Trends in Neural Computation, vol. 418, pp. 391–418. Springer, Heidelberg (2007)
Muñoz, A., Sánchez-Úbeda, E., Cruz, A., Marín, J.: Short-term forecasting in power systems: a guided tour. In: Rebennack, S., Pardalos, P., Pereira, M., Iliadis, N. (eds.) Handbook of Power Systems II, pp. 129–160. Springer, Heidelberg (2010)
Hippert, H., Pedreira, C., Souza, R.: Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans. Power Syst. 16(1), 44–55 (2001)
Gross, G., Galiana, F.: Short-term load forecasting. Proc. IEEE 75(12), 1558–1573 (1987)
Bessa, R.J., Trindade, A., Miranda, V.: Spatial-temporal solar power forecasting for smart grids. IEEE Trans. Ind. Inform. 11(1), 232–241 (2015)
Huang, R., Huang, T., Gadh, R., Li, N.: Solar generation prediction using the ARMA model in a laboratory-level micro-grid. In: 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), pp. 528–533. IEEE, November 2012
Charytoniuk, W., Chen, M.S.: Very short-term load forecasting using artificial neural networks. IEEE Trans. Power Syst. 15(1), 263–268 (2000)
Guan, C., Luh, P.B., Michel, L.D., Wang, Y., Friedland, P.B.: Very short-term load forecasting: wavelet neural networks with data pre-filtering. IEEE Trans. Power Syst. 28(1), 30–41 (2013)
Asber, D., Lefebvre, S., Saad, M., Desbiens, C.: Modeling of distribution loads for short and medium-term load forecasting. In: 2007 IEEE Power Engineering Society General Meeting, pp. 1–5. IEEE, June 2007
Kandil, M.S., El-Debeiky, S.M., Hasanien, N.E.: Long-term load forecasting for fast-developing utility using a knowledge-based expert system. IEEE Power Eng. Rev. 22(4), 78–78 (2002)
Zhang, H.T., Xu, F.Y., Zhou, L.: Artificial neural network for load forecasting in smart grid. In: 2010 International Conference on Machine Learning and Cybernetics, pp. 3200–3205. IEEE, July 2010
Zhang, Z., Ye, S.: Long term load forecasting and recommendations for china based on support vector regression. In: 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 597–602. IEEE, November 2011
Daneshi, H., Shahidehpour, M., Choobbari, A.L.: Long-term load forecasting in electricity market. In: 2008 IEEE International Conference on Electro/Information Technology, pp. 395–400. IEEE, May 2008
Gullo, F., Ponti, G., Tagarelli, A., liritano, S., Ruffolo, M., Labate, D.: Low-voltage electricity customer profiling based on load data clustering. In: Proceedings of the 2009 International Database Engineering & #38; Applications Symposium, IDEAS 2009, pp. 330–333. ACM, New York (2009)
Joe-Wong, C., Sen, S., Ha, S., Chiang, M.: Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility. IEEE J. Sel. Areas Commun. 30(6), 1075–1085 (2012)
Borges, C.E., Penya, Y.K., Fernandez, I.: Evaluating combined load forecasting in large power systems and smart grids. IEEE Trans. Ind. Inform. 9(3), 1570–1577 (2013)
Erol-Kantarci, M., Hussein, T.M.: Prediction-based charging of PHEVs from the smart grid with dynamic pricing. In: IEEE Local Computer Network Conference, pp. 1032–1039. IEEE, October 2010
Chassin, D.P., Schneider, K., Gerkensmeyer, C.: GridLAB-D: an open-source power systems modeling and simulation environment. In: 2008 IEEE/PES Transmission and Distribution Conference and Exposition, pp. 1–5. IEEE, April 2008
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008). ISBN 3-900051-07-0
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ferreira, A., Leitão, P., Barata, J. (2017). Prediction Models for Short-Term Load and Production Forecasting in Smart Electrical Grids. In: Mařík, V., Wahlster, W., Strasser, T., Kadera, P. (eds) Industrial Applications of Holonic and Multi-Agent Systems. HoloMAS 2017. Lecture Notes in Computer Science(), vol 10444. Springer, Cham. https://doi.org/10.1007/978-3-319-64635-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-64635-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64634-3
Online ISBN: 978-3-319-64635-0
eBook Packages: Computer ScienceComputer Science (R0)