Data Analytics for Load and Price Forecasting via Enhanced Support Vector Regression

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


In this paper, month-ahead electricity load and price forecasting is done to achieve accuracy. The data of electricity load is taken from the Smart Meter (SM) in London. Electricity load data of five months is taken from one block SM along with the weather data. Data Analytics (DA) techniques are used in the paper for month-ahead electricity load and price prediction. In this paper, forecasting is done in multiple stages. At first stage, feature extraction and selection is performed to make data suitable for efficient forecasting and to reduce complexity of data. After that, regression techniques are used for prediction. Singular Value Decomposition (SVD) is used for feature extraction afterwards; feature selection is done in two-stages, by using Random Forest (RF) and Recursive Feature Elimination (RFE). For electricity load and price forecasting Logistic Regression (LR), Support Vector Regression (SVR) is used. Moreover forecasting is done by the proposed technique Enhanced Support Vector Regression (EnSVR), which is modified from SVR. Simulation results show that the proposed system gives more accuracy in load and price prediction.


Load forecasting Price forecasting Data Analytics Logistic Regression Support Vector Regression Enhanced Support Vector Regression 


  1. 1.
    Zhang, D., Li, S., Sun, M., O’Neill, Z.: An optimal and learning-based demand response and home energy management system. IEEE Trans. Smart Grid 7(4), 1790–1801 (2016)CrossRefGoogle Scholar
  2. 2.
    Shafie-khah, M., Siano, P.: A stochastic home energy management system considering satisfaction cost and response fatigue. IEEE Trans. Industr. Inf. 14(2), 629–638 (2017)CrossRefGoogle Scholar
  3. 3.
    Akhavan-Rezai, E., Shaaban, M.F., El-Saadany, E.F., Karray, F.: Online intelligent demand management of plug-in electric vehicles in future smart parking lots. IEEE Syst. J. 10(2), 483–494 (2016)CrossRefGoogle Scholar
  4. 4.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Jayabarathi, T., Raghunathan, T., Adarsh, B.R., Suganthan, P.N.: Economic dispatch using hybrid grey wolf optimizer. Energy 111, 630–641 (2016)CrossRefGoogle Scholar
  6. 6.
    Geem, Z.W., Yoon, Y.: Harmony search optimization of renewable energy charging with energy storage system. Int. J. Electr. Power Energy Syst. 86, 120–126 (2017)CrossRefGoogle Scholar
  7. 7.
    Ouyang, H.B., Gao, L.Q., Kong, X.Y., Li, S., Zou, D.X.: Hybrid harmony search particle swarm optimization with global dimension selection. Inf. Sci. 346, 318–337 (2016)CrossRefGoogle Scholar
  8. 8.
    Ambia, M.N., Hasanien, H.M., Al-Durra, A., Muyeen, S.M.: Harmony search algorithm-based controller parameters optimization for a distributed-generation system. IEEE Trans. Power Delivery 30(1), 246–255 (2015)CrossRefGoogle Scholar
  9. 9.
    Manzoor, A., Javaid, N., Ullah, I., Abdul, W., Almogren, A., Alamri, A.: An intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes. Energies 10(9), 1258 (2017)CrossRefGoogle Scholar
  10. 10.
    Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  11. 11.
    Mahmood, D., Javaid, N., Ahmed, I., Alrajeh, N., Niaz, I.A., Khan, Z.A.: Multi-agent-based sharing power economy for a smart community. Int. J. Energy Res. 41(14), 2074–2090 (2017)CrossRefGoogle Scholar
  12. 12.
    Zhao, Z., Lee, W.C., Shin, Y., Song, K.B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)CrossRefGoogle Scholar
  13. 13.
    Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)CrossRefGoogle Scholar
  14. 14.
    Rajalingam, S., Malathi, V.: HEM algorithm based smart controller for home power management system. Energy Buildings 131, 184–192 (2016)CrossRefGoogle Scholar
  15. 15.
    Ahmed, M.S., Mohamed, A., Khatib, T., Shareef, H., Homod, R.Z., Ali, J.A.: Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm. Energy Buildings 138, 215–227 (2017)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Li, C., Li, L.: Wavelet transform and Kernel-based extreme learning machine for electricity price forecasting. Energy Syst. 9(1), 113–134 (2018)CrossRefGoogle Scholar
  17. 17.
    Förderer, K., Ahrens, M., Bao, K., Mauser, I., Schmeck, H.: Towards the modeling of flexibility using artificial neural networks in energy management and smart grids: note. In: Proceedings of the Ninth International Conference on Future Energy Systems, pp. 85–90. ACM (2018)Google Scholar
  18. 18.
    Gao, W., Darvishan, A., Toghani, M., Mohammadi, M., Abedinia, O., Ghadimi, N.: Different states of multi-block based forecast engine for price and load prediction. Int. J. Electr. Power Energy Syst. 104, 423–435 (2019)CrossRefGoogle Scholar
  19. 19.
    Nowotarski, J., Weron, R.: Recent advances in electricity price forecasting: a review of probabilistic forecasting. Renew. Sustain. Energy Rev. (2017)Google Scholar
  20. 20.
    Bramer, L.M., Rounds, J., Burleyson, C.D., Fortin, D., Hathaway, J., Rice, J., Kraucunas, I.: Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days. Appl. Energy 205, 1408–1418 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.Computer Information ScienceHigher Colleges of TechnologyFujairahUAE
  3. 3.Virtual University of PakistanAzad KashmirPakistan

Personalised recommendations