Abstract
In electricity market, electricity price has some complicated features like high volatility, non-linearity and non-stationarity that make very difficult to predict the accurate price. However, it is necessary for markets and companies to predict accurate electricity price. In this paper, we enhanced the forecasting accuracy by combined approaches of Kernel Extreme Learning Machine (KELM) and Autoregression Moving Average (ARMA) along with unique and enhanced features of both models. Wavelet transform is applied on prices series to decompose them, afterward test has performed on decomposed series for providing stationary series to AMRA-model and non-stationary series to KELM-model. At the end series are tuned with our combine approach of enhanced price prediction. The performance of our enhanced combined method is evaluated by electricity price dataset of New South Wales (NSW), Australian market. The simulation results show that combined method has more accurate prediction than individual methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cerjan, M., Krzelj, I., Vidak, M., Delimar, M.: A literature review with statistical analysis of electricity price forecasting methods. In: 2013 IEEE EUROCON, pp. 756–763. IEEE (2013)
Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30, 1030–1081 (2014)
Chan, S.-C., Tsui, K.M., Wu, H., Hou, Y., Wu, Y.-C., Wu, F.F.: Load/price forecasting and managing demand response for smart grids: methodologies and challenges. IEEE Signal Process. Mag. 29, 68–85 (2012)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 Proceedings 2004 IEEE International Joint Conference on Neural Networks, pp. 985–990. IEEE (2004)
Lago, J., De Ridder, F., De Schutter, B.: Forecasting spot electricity prices: deep learning approaches and empirical comparison of traditional algorithms. Appl. Energy 221, 386–405 (2018)
Lago, J., De Ridder, F., Vrancx, P., De Schutter, B.: Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Appl. Energy 211, 890–903 (2018)
Loi, T.S.A., Le Ng, J.: Anticipating electricity prices for future needs-Implications for liberalised retail markets. Appl. Energy 212, 244–264 (2018)
Bento, P.M.R., Pombo, J.A.N., Calado, M.R.A., Mariano, S.J.P.S.: A bat optimized neural network and wavelet transform approach for short-term price forecasting. Appl. Energy 210, 88–97 (2018)
Shuja, S.M., Javaid, N., Rafique, M.Z.: Towards Efficient Scheduling of Smart Appliances for Energy Management by Candidate Solution Updation Algorithm (CSUA) in Smart Grid
Chitsaz, H., Zamani-Dehkordi, P., Zareipour, H., Parikh, P.P.: Electricity price forecasting for operational scheduling of behind-the-meter storage systems. IEEE Trans. Smart Grid 9(6), 6612–6622 (2018)
Raa, P., Vilar, J., Aneiros, G.: On the use of functional additive models for electricity demand and price prediction. IEEE Access 6, 9603–9613 (2018)
Alanis, A.Y.: Electricity prices forecasting using artificial neural networks. IEEE Lat. Am. Trans. 16(1), 105–111 (2018)
Ahmad, A., Javaid, N., Mateen, A., Awais, M., Khan, Z.: Short-term load forecasting in smart grids: an intelligent modular approach. Energies 12(1), 164 (2019)
Khan, M., Javaid, N., Naseem, A., Ahmed, S., Riaz, M., Akbar, M., Ilahi, M.: Game theoretical demand response management and short-term load forecasting by knowledge based systems on the basis of priority index. Electronics 7(12), 431 (2018)
Esther, B.P., Kumar, K.S.: A survey on residential demand side management architecture, approaches, optimization models and methods. Renew. Sustain. Energy Rev. 59, 342–351 (2016)
AEMO. http://www.aemo.com.au/Electricity/Data/Price-and-Demand/Aggregated-Price-and-Demand-Data-Files
Huang, G.-B., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71, 34608 (2008)
Box, G.E., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shuja, S.M. et al. (2019). Electricity Price Prediction by Enhanced Combination of Autoregression Moving Average and Kernal Extreme Learing Machine. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_110
Download citation
DOI: https://doi.org/10.1007/978-3-030-15035-8_110
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15034-1
Online ISBN: 978-3-030-15035-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)