Abstract
One of the key issues in the Smart Grid (SG) is accurate electric load forecasting. Energy generation and consumption have highly varying. Accurate forecasting of electric load can decrease the fluctuating behavior between energy generation and consumption. By knowing the upcoming electricity load consumption, we can control the extra energy generation. To solve this issue, we have proposed a forecasting model, which consists of a two-stage process; feature engineering and classification. Feature engineering consists of feature selection and extraction. By combining Extreme Gradient Boosting (XGBoost) and Decision Tree (DT) techniques, we have proposed a hybrid feature selector to minimize the feature redundancy. Furthermore, Recursive Feature Elimination (RFE) technique is applied for dimension reduction and improve feature selection. To forecast electric load, we have applied Support Vector Machine (SVM) set tuned with three super parameters, i.e., kernel parameter, cost penalty, and incentive loss function parameter. Electricity market data is used in our proposed model. Weekly and months ahead forecasting experiments are conducted by proposed model. Forecasting performance is assessed by using RMSE and MAPE and their values are 1.682 and 12.364. The simulation results show 98% load forecasting accuracy.
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References
Kailas, A., Cecchi, V., Mukherjee, A.: A survey of communications and networking technologies for energy management in buildings and home automation. J. Comput. Netw. Commun. 2012, 12 (2012)
Iqbal, Z., Javaid, N., Iqbal, S., Aslam, S., Khan, Z.A., Abdul, W., Almogren, A., Alamri, A.: A domestic microgrid with optimized home energy management system. Energies 11(4), 1002 (2018)
Iqbal, Z., Javaid, N., Mohsin, S., Akber, S., Afzal, M., Ishmanov, F.: Performance analysis of hybridization of heuristic techniques for residential load scheduling. Energies 11(10), 2861 (2018)
Rahim, M.H., Javaid, N., Shafiq, S., Iqbal, M.N., Khalid, M.U., Memon, U.U.: Exploiting heuristic techniques for efficient energy management system in smart grid. In: 2018 14th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 54–59. IEEE (2018)
Xiang-ting, C., Yu-hui, Z., Wei, D., Jie-bin, T., Yu-xiao, G.: Design of intelligent demand side management system respond to varieties of factors. In: 2010 China International Conference on Electricity Distribution (CICED), pp. 1–5. IEEE (2010)
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)
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)
Wang, K., Yu, J., Yu, Y., Qian, Y., Zeng, D., Guo, S., Xiang, Y., Wu, J.: A survey on energy internet: architecture, approach and emerging technologies. IEEE Syst. J. (2017)
Jiang, H., Wang, K., Wang, Y., Gao, M., Zhang, Y.: Energy big data: a survey. IEEE Access 4, 3844–3861 (2016)
Alam, M.R., Reaz, M.B.I., Ali, M.A.M.: A review of smart homes—past, present, and future. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 1190–1203 (2012)
Zhu, Q., Han, Z., Başar, T.: A differential game approach to distributed demand side management in smart grid. In: 2012 IEEE International Conference on Communications (ICC), pp. 3345–3350. IEEE (2012)
Soares, J., Silva, M., Sousa, T., Vale, Z., Morais, H.: Distributed energy resource short-term scheduling using signaled particle swarm optimization. Energy 42(1), 466–476 (2012)
Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid, pp. 1–5 (2012)
Liu, J., Li, C.: 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)
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)
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)
Ryu, S., Noh, J., Kim, H.: Deep neural network based demand side short term load forecasting. Energies 10(1), 3 (2016)
Fan, C., Xiao, F., Zhao, Y.: A short-term building cooling load prediction method using deep learning algorithms. Appl. Energy 195, 222–233 (2017)
Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)
Moghaddass, R., Wang, J.: A hierarchical framework for smart grid anomaly detection using large-scale smart meter data. IEEE Trans. Smart Grid 9(6), 5820–5830 (2018)
Zhao, J.H., Dong, Z.Y., Li, X.: Electricity price forecasting with effective feature preprocessing. In: IEEE Power Engineering Society General Meeting, p. 8-pp. IEEE (2006)
Qiu, Z.-W.: Mutivariable mutual information based feature selection for electricity price forecasting. In: 2012 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 168–173. IEEE (2012)
Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–74 (2017)
Qian, H., Qiu, Z.: Feature selection using C4.5 algorithm for electricity price prediction. In: 2014 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 175–180. IEEE (2014)
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Ayub, N., Javaid, N., Mujeeb, S., Zahid, M., Khan, W.Z., Khattak, M.U. (2020). Electricity Load Forecasting in Smart Grids Using Support Vector Machine. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_1
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DOI: https://doi.org/10.1007/978-3-030-15032-7_1
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