Abstract
Designers are required to plan for future expansion and also to estimate the grid’s future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid’s utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.
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References
AlFuhaid, A.S., El-Sayed, M.A., Mahmoud, M.S., 1997. Cascaded artificial neural networks for short-term load forecasting. IEEE Trans. on Power Syst., 12(4):1524–1529. [doi:10.1109/59.627852]
Anderson, B.D.O., Moore, J.B., 1979. Optimal Filtering. Prentice Hall, New Jersey.
Beligiannis, G., Skarlas, L., Likothanassis, S., 2004. A generic applied evolutionary hybrid technique. IEEE Signal Processing Mag., 21(3):28–38. [doi:10.1109/MSP.2004.1296540]
Charytoniuk, W., Chen, M.S., Kotas, P., van Olinda, P., 1999. Demand forecasting in power distribution systems using nonparametric probability density estimation. IEEE Trans. on Power Syst., 14(4):1200–1206. [doi:10.1109/59.801873]
Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J., 2003. ARIMA models to predict next-day electricity prices. IEEE Trans. on Power Syst., 18(3):1014–1020. [doi:10.1109/TPWRS.2002.804943]
Darbellay, G.A., Slama, M., 2000. Forecasting the short-term demand for electricity: Do neural networks stand a better chance? Int. J. Forecast., 16(1):71–83. [doi:10.1016/S0169-2070(99)00045-X]
di Caprio, U., Genesio, R., Pozzi, S., Vicino, A., 2006. Short term load forecasting in electric power systems: a comparison of ARMA models and extended wiener filtering. J. Forecast., 2(1):59–76. [doi:10.1002/for.3980020107]
Ediger, V.S., Akar, S., 2007. ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3):1701–1708. [doi:10.1016/j.enpol.2006.05.009]
Espinoza, M., Joye, C., Belmans, R., DeMoor, B., 2005. Short-term load forecasting, profile identification and customer segmentation: a methodology based on periodic time series. IEEE Trans. on Power Syst., 20(3):1622–1630. [doi:10.1109/TPWRS.2005.852123]
Haida, T., Muto, S., 1994. Regression based peak load forecasting using a transformation technique. IEEE Trans. on Power Syst., 9(4):1788–1794. [doi:10.1109/59.331433]
Huang, S.J., Shih, K.R., 2003. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans. on Power Syst., 18(2):673–679. [doi:10.1109/TPWRS.2003.811010]
Katsikas, S.K., Likothanassis, S.D., Beligiannis, G.N., Berkeris, K.G., Fotakis, D.A., 2001. Genetically determined variable structure multiple model estimation. IEEE Trans. on Signal Processing, 49(10):2253–2261. [doi:10.1109/78.950781]
Lainiotis, D.G., 1971. Optimal adaptive estimation: structure and parameter adaptation. IEEE Trans. on Automatic Control, 16(2):160–170. [doi:10.1109/TAC.1971.1099684]
Lainiotis, D.G., 1976a. Partitioning: a unifying framework for adaptive systems, I: estimation. Proc. IEEE, 64(8):1126–1143. [doi:10.1109/PROC.1976.10284]
Lainiotis, D.G., 1976b. Partitioning: a unifying framework for adaptive systems, II: control. Proc. IEEE, 64(8):1182–1198. [doi:10.1109/PROC.1976.10289]
Lainiotis, D.G., Papaparaskeva, P., 1998. A partitioned adaptive approach to nonlinear channel equalization. IEEE Trans. on Commun., 46(10):1325–1336. [doi:10.1109/26.725310]
Lu, J.C., Niu, D.X., Jia, Z.Y., 2004. A Study of Short-term Load Forecasting Based on ARIMA-ANN. Proc. Int. Conf. on Machine Learning and Cybernetics, 5:3183–3187.
Moussas, V.C., Katsikas, S.K., 2005. A Multi-Model Approach to Fatigue Crack Growth Monitoring and Prediction. 12th Int. Workshop on Systems, Signals & Image Processing, Chalkida, Greece, p.57–61.
Moussas, V.C., Pappas, S.Sp., 2005. Adaptive Network anomaly Detection Using Bandwidth Utilization Data. 1st Int. Conf. on Experiments/Processes/System Modelling/Simulation/Optimization, Athens, Greece.
Moussas, V.C., Likothanassis, S.D., Katsikas, S.K., Leros, A.K., 2005. Adaptive On-line Multiple Source Detection. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 4:1029–1032.
Nikitakos, N.V., Leros, A.K., Katsikas, S.K., 1998. Towed array shape estimation using multimodel partitioning filters. IEEE J. Ocean. Eng., 23(4):380–384. [doi:10.1109/48.725232]
Nowicka-Zagrajek, J., Weron, R., 2002. Modeling electricity loads in California: ARMA models with hyperbolic noise. Signal Processing, 82(12):1903–1915. [doi:10.1016/S0165-1684(02)00318-3]
Pappas, S.Sp., Leros, A.K., Katsikas, S.K., 2006. Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory. Digital Signal Processing, 16(6):782–795. [doi:10.1016/j.dsp.2006.06.006]
Pino, R., Parreno, J., Gomez, A., Priore, P., 2008. Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Eng. Appl. Artif. Intell., 21(1):53–62. [doi:10.1016/j.engappai.2007.02.001]
PPC S.A., 2006. Annual Electrical Energy’s Statistical and Economical Data. Hellenic Public Power Corporation S.A., Athens, Greece.
Rajesh, K.G., 1997. Expert systems: their implications and applications for power systems. IEEE Potentials, 16(2):35–37. [doi:10.1109/45.580448]
Sisworahardjo, N.S., El-Keib, A.A., Choi, J., Valenzuela, J., Brooks, R., El-Agtal, I., 2006. A stochastic load model for an electricity market. Electr. Power Syst. Res., 76(6–7):500–508. [doi:10.1016/j.epsr.2005.02.009]
Yap, K.S., Abidin, I.Z., Lim, C.P., Shah, M.S., 2006. Short Term Load Forecasting Using a Hybrid Neural Network. IEEE Int. Power and Energy Conf., p.123–128. [doi:10.1109/PECON.2006.346632]
Zhou, M., Yan, Z., Ni, Y., Li, G., 2004. An ARIMA Approach to Forecasting Electricity Price with Accuracy Improvement by Predicted Errors. IEEE Power Engineering Society General Meeting, 1:233–238.
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Pappas, S.S., Ekonomou, L., Moussas, V.C. et al. Adaptive load forecasting of the Hellenic electric grid. J. Zhejiang Univ. Sci. A 9, 1724–1730 (2008). https://doi.org/10.1631/jzus.A0820042
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DOI: https://doi.org/10.1631/jzus.A0820042
Key words
- Adaptive multi-model filtering
- ARIMA
- Load forecasting
- Measurements
- Kalman filter
- Order selection
- Seasonal variation
- Parameter estimation