Abstract
The purpose of the short-term electricity demand prediction is to forecast in advance the system load, represented by the sum of all consumers load at the same time. Power demand forecasting is important for economically efficient operation and effective control of power systems and enables to plan the load of generating unit. A precise load forecasting is required to avoid high generation cost and the spinning reserve capacity. Under-prediction of the demands leads to an insufficient reserve capacity preparation and can threaten the system stability, on the other hand, over-prediction leads to an unnecessarily large reserve that leads to a high cost preparations. Differential polynomial neural network is a new neural network type, which forms and resolves an unknown general partial differential equation of an approximation of a searched function, described by data observations. It generates convergent sum series of relative polynomial derivative terms, which can substitute for the ordinary differential equation, describing 1-parametric function time-series with partial derivatives. A new method of the short-term power demand forecasting, based on similarity relations of subsequent day progress cycles at the same time points is presented and tested on 2 datasets. Comparisons were done with the artificial neural network using the same prediction method. Experimental results indicate that proposed method using the differential polynomial network is efficient.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abraham, A.: Special issue: Hybrid approaches for approximate reasoning. Journal of Intelligent and Fuzzy Systems 23(2-3), 41–42 (2012)
Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Statistical Science 8(1), 10–15 (1993)
Chan, K., Chau, W.Y.: Mathematical theory of reduction of physical parameters and similarity analysis. International Journal of Theoretical Physics 18, 835–844 (1979)
Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A., Snásel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)
Darbellay, G.A., Slama, M.: Forecasting the short-term demand for electricity. International Journal of Forecasting 16, 71–83 (2000)
Garcıa-Ascanio, K., Mate, C.: Electric power demand forecasting using interval time series. Energy Policy 38, 715–725 (2010)
Hippert, H.S., Pedreira, C.E., Souza, R.C.: Neural Networks for Short-Term Load Forecasting: A Review and Evaluation. IEEE Transactions on Power Systems 16(1) (2001)
Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Transactions on Systems, SMC-1(4) (1971)
Mamlook, R., Badran, O., Abdulhadi, E.: A fuzzy inference model for short-term load forecasting. Energy Policy 37, 1239–1248 (2009)
Nikolaev, N.Y., Iba, H.: Adaptive Learning of Polynomial Networks. Springer (2006)
Nikolaev, N.Y., Iba, H.: Polynomial harmonic GMDH learning networks for time series modelling. Neural Networks 16, 1527–1540 (2003)
Unsihuay-Vila, C., Zambroni, A.C., Marangon-Lima, J.W., Balestrassi, P.P.: Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model. Electrical Power and Energy Systems 32, 108–116 (2010)
Zjavka, L.: Recognition of Generalized Patterns by a Differential Polynomial Neural Network. Engineering, Technology & Applied Science Research 2(1) (2012)
National Grid, U.K. Electricity Transmission, http://www2.nationalgrid.com/UK/Industry-information/Electricity-transmission-operational-data/Data-explorer/
ANEX 54, Measured Canadian Occupant-driven Electrical Load Profiles, http://www.iea-annex54.org/annex42/data.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zjavka, L. (2014). Daily Power Load Forecasting Using the Differential Polynomial Neural Network. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-07617-1_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07616-4
Online ISBN: 978-3-319-07617-1
eBook Packages: Computer ScienceComputer Science (R0)