Advertisement

Electricity Load and Price Forecasting Using Enhanced Machine Learning Techniques

  • Hamida Bano
  • Aroosa Tahir
  • Ishtiaq Ali
  • Raja Jalees ul Hussen Khan
  • Abdul Haseeb
  • Nadeem JavaidEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 994)

Abstract

The exponential increase in electricity generation and consumption pattern are the two main issues in the wholesale markets. To handle these issues different machine learning techniques are used for load and price prediction in the research field. The wholesale utilities provide real-time data of load and price for the better prediction of electricity generation purposes. The New York Independent System Operator (NY-ISO) is one of the utility which provide electricity to different counties like United States, Canada and Israel. In this paper, hourly data of 2016–2017 is used for the forecasting process of load and price of New York City. Feature selection and extraction are used to achieve important features. The feature selection is done by two techniques Classification and Regression Tree (CART) and Recursive Feature Elimination (RFE) and Feature extraction by using Singular Value Decomposition (SVD). The Multiple Layer Perceptron (MLP), Support Vector Machine (SVM) and Logistic Regression (LR) classifiers are separately used for forecasting purposes of electricity load and price. Further enhance these three techniques EMLP, ESVM and ELR to take more accurate results for electricity load and price forecasting.

References

  1. 1.
    Jindal, A., Singh, M., Kumar, N.: Consumption-aware data analytical demand response scheme for peak load reduction in smart grid. IEEE Trans. Ind. Electron. 65, 8993–9004 (2018)Google Scholar
  2. 2.
    Liu, C., Jin, Z., Gu, J., Qiu, C.: Short-term load forecasting using a long short-term memory network. In: Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017 IEEE PES, pp. 1–6. IEEE (2017)Google Scholar
  3. 3.
    Zheng, J., Xu, C., Zhang, Z., Li, X.: Electric load forecasting in smart grids using long-short-term-memory based recurrent neural network. In: 2017 51st Annual Conference on Information Sciences and Systems (CISS), pp. 1–6. IEEE (2017)Google Scholar
  4. 4.
    Wang, F., Li, ., Zhou, L., Ren, H., Contreras, J., Shafie-Khah, M., Catalão, J.P.: Daily pattern prediction based classification modeling approach for day-ahead electricity price forecasting. Int. J. Electr. Power Energy Syst. 105, 529–540 (2019)Google Scholar
  5. 5.
    Raviv, E., Bouwman, K.E., van Dijk, D.: Forecasting day-ahead electricity prices: utilizing hourly prices. Energy Econ. 50, 227–239 (2015)Google Scholar
  6. 6.
    Mosbah, H., El-Hawary, M.: Hourly electricity price forecasting for the next month using multilayer neural network. Can. J. Electr. Comput. Eng. 39(4), 283–291 (2016)CrossRefGoogle Scholar
  7. 7.
    Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data (2017)Google Scholar
  8. 8.
    Chahkoutahi, F., Khashei, M.: A seasonal direct optimal hybrid model of computational intelligence and soft computing techniques for electricity load forecasting. Energy 140, 988–1004 (2017)CrossRefGoogle Scholar
  9. 9.
    Ahmad, A., Javaid, N., Guizani, M., Alrajeh, N., Khan, Z.A.:. An accurate and fast converging short-term load forecasting model for industrial applications in a smart grid. IEEE Trans. Ind. Inf. 13(5), 2587–2596 (2017)Google Scholar
  10. 10.
    Liu, J.P., Li, C.L.: The short-term power load forecasting based on sperm whale algorithm and wavelet least square support vector machine with DWT-IR for feature selection. Sustainability 9(7), 1188 (2017)CrossRefGoogle Scholar
  11. 11.
    Rafiei, M., Niknam, T., Khooban, M.-H.: Probabilistic forecasting of hourly electricity price by generalization of ELM for usage in improved wavelet neural network. IEEE Trans. Ind. Inform. 13(1), 71–79 (2017)CrossRefGoogle Scholar
  12. 12.
    Keles, D., Scelle, J., Paraschiv, F., Fichtner, W.: Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy 162, 218–230 (2016)CrossRefGoogle Scholar
  13. 13.
    Feng, C., Cui, M., Hodge, B.-M., Zhang, J.: A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl. Energy 190, 1245–1257 (2017)CrossRefGoogle Scholar
  14. 14.
    Wang, H.Z., Wang, G.B., Li, G.Q., Peng, J.C., Liu, Y.T.: Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl. Energy 182, 80–93 (2016)CrossRefGoogle Scholar
  15. 15.
    Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for electricity price forecasting in the smart grid. IEEE Trans. Big Data (2017)Google Scholar
  16. 16.
    Agrawal, R.K., Muchahary, F., Tripathi, M.M.: Long term load forecasting with hourly predictions based on long-short-term-memory networks. In: Texas Power and Energy Conference (TPEC), 2018 IEEE, pp. 1–6. IEEE (2018)Google Scholar
  17. 17.
    Abedinia, O., Amjady, N., Ghadimi, N.: Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34(1), 241–260 (2018)Google Scholar
  18. 18.
    Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)Google Scholar
  19. 19.
    Chen, Y., Li, M., Yang, Y., Li, C., Li, Y., Li, L.: A hybrid model for electricity price forecasting based on least square support vector machines with combined kernel. J. Renew. Sustain. Energy 10(5), 055502 (2018)Google Scholar
  20. 20.
    Liu, Y., Wang, W., Ghadimi, N.: Electricity load forecasting by an improved forecast engine for building level consumers. Energy 139, 18–30 (2017)CrossRefGoogle Scholar
  21. 21.
    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
  22. 22.
    Shayeghi, H., Ghasemi, A., Moradzadeh, M., Nooshyar, M.: Day-ahead electricity price forecasting using WPT, GMI and modified LSSVM-based S-OLABC algorithm. Soft Comput. 21(2), 525–541 (2017)Google Scholar
  23. 23.
    Khwaja, A.S., Naeem, M., Anpalagan, A., Venetsanopoulos, A., Venkatesh, B.: Improved short-term load forecasting using bagged neural networks. Electric Power Syst. Res. 125, 109–115 (2015)CrossRefGoogle Scholar
  24. 24.
    Ghasemi, A., Shayeghi, H., Moradzadeh, M., Nooshyar, M.: A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management. Appl. Energy 177, 40–59 (2016)CrossRefGoogle Scholar
  25. 25.
    Coelho, V.N., Coelho, I.M., Coelho, B.N., Reis, A.J.R., Enayatifar, R., Souza, M.J.F., Guimarães, F.G.: A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment. Appl. Energy 169, 567–584 (2016)Google Scholar
  26. 26.
    Tarsitano, A., Amerise, I.L.: Short-term load forecasting using a two-stage sarimax model. Energy 133, 108–114 (2017)CrossRefGoogle Scholar
  27. 27.
    Dongxiao, N., Tiannan, M., Bingyi, L.: Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm. J. Comb. Optim. 33(3), 1122–1143 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hamida Bano
    • 1
  • Aroosa Tahir
    • 2
  • Ishtiaq Ali
    • 1
  • Raja Jalees ul Hussen Khan
    • 1
  • Abdul Haseeb
    • 1
  • Nadeem Javaid
    • 1
    Email author
  1. 1.COMSATS University IslamabadIslamabadPakistan
  2. 2.Sardar Bhadur Khan Women University QuettaQuettaPakistan

Personalised recommendations