Abstract
For efficient use of smart grid, exact prediction about the in-future coming load is of great importance to the utility. In this proposed scheme initially we converted daily Australian energy market operator load data to weekly data time series. Furthermore, we used eXtreme Gradient Boosting (XGBoost) for extracting features from the data. After feature selection we used XGBoost for the purpose of forecasting the electricity load for single time lag. XGBoost perform extremely well for time series prediction with efficient computing time and memmory resources usage. Our proposed scheme outperformed other schemes for mean average percentage error metric.
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References
Mathaba, T., Xia, X., Zhang, J.: Analysing the economic benefit of electricity price forecast in industrial load scheduling. Electr. Power Syst. Res. 116, 158–165 (2014)
Sarada, K., Bapiraju, V.: Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm. In: Proceeding of the International Conference on Smart Electric Grid, pp. 1–5 (2014)
Shafie-Khah, M., Moghaddam, M.P., Sheikh-El-Eslami, M.: Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Convers. Manag. 52(5), 2165–2169 (2011)
Garcia, R.C., Contreras, J., Van Akkeren, M., Garcia, J.B.C.: A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans. Power Syst. 20(2), 867–874 (2005)
Shahidehpour, M., Yamin, H., Li, Z.: Market overview in electric power systems. In: Market Operations in Electric Power Systems, pp. 1–20. Wiley, New York (2002)
Ci-wei, G., Bompard, E., Napoli, R., Cheng, H.: Price forecast in the competitive electricity market by support vector machine. Phys. A: Stat. Mech. Appl. 382(1), 98–113 (2007)
Cai, Y., Lin, J., Wan, C., Song, Y.: A stochastic Bi-level trading model for an active distribution company with distributed generation and interruptible loads. IET Renew. Power Gener. 11(2), 278–288 (2017)
Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30(4), 1030–1081 (2014)
Hu, L., Taylor, G.: A novel hybrid technique for short-term electricity price forecasting in UK electricity markets. J. Int. Counc. Electr. Eng. 4(2), 114–120 (2014)
Voronin, S., Partanen, J.: Forecasting electricity price and demand using a hybrid approach based on wavelet transform, ARIMA and neural networks. Int. J. Energy Res. 38(5), 626–637 (2014)
Kou, P., Liang, D., Gao, L., Lou, J.: Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning. Energy Convers. Manag. 89, 298–308 (2015)
Shrivastava, N.A., Panigrahi, B.K.: A hybrid wavelet-ELM based short term price forecasting for electricity markets. Int. J. Electr. Power Energy Syst. 55, 41–50 (2014)
He, K., Xu, Y., Zou, Y., Tang, L.: Electricity price forecasts using a curvelet denoising based approach. Phys. A: Stat. Mech. Appl. 425, 1–9 (2015)
Wan, C., Xu, Z., Wang, Y., Dong, Z.Y., Wong, K.P.: A hybrid approach for probabilistic forecasting of electricity price. IEEE Trans. Smart Grid 5(1), 463–470 (2014)
Wan, C., Niu, M., Song, Y., Xu, Z.: Pareto optimal prediction intervals of electricity price. IEEE Trans. Power Syst. 32(1), 817–819 (2017)
Liu, B., Nowotarski, J., Hong, T., Weron, R.: Probabilistic load forecasting via quantile regression averaging on sister forecasts. IEEE Trans. Smart Grid 8(2), 1 (2017). https://doi.org/10.1109/tsg.2015.2437877
Wanga, P., Liu, B., Hongb, T.: Electric load forecasting with recency effect: a big data approach. Int. J. Forecast. 32, 585–597 (2016)
Chou, J.-S., Ngo, N.-T.: Smart grid data analytics framework for increasing energy savings in residential buildings. Autom. Constr. 72, 247–257 (2016)
Ludwig, N., Feuerriegel, S., Neumann, D.: Putting big data analytics to work: feature selection for forecasting electricity prices using the LASSO and random forests. ISSN 1246-0125 (Print) 2116-7052
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)
Wang, L., Zhang, Z., Chen, J.: Short-term electricity price forecasting with stacked denoising autoencoders. IEEE Trans. Power Syst. 32(4), 2673–2681 (2017)
Lagoa, 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(1), 890–903 (2018)
Raviv, E., Bouwman, K.E., van Dijk, D.: Forecasting day-ahead electricity prices: utilizing hourly prices. Energy Econ. 50, 227–239 (2015)
Xiao, L., Jianzhou Wang, R., Hou, J.W.: A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82(15), 524–549 (2015)
Singh, S., Yassine, A.: Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies 11, 452 (2018)
Moon, J., Kim, K.-H., Kim, Y., Hwang, E.: A short-term electric load forecasting scheme using 2-stage predictive analytics. In: 2018 IEEE International Conference on Big Data and Smart Computing (2018)
González, J.P., San Roque, A.M., Perez, E.A.: Forecasting functional time series with a new Hilbertian ARMAX model: application to electricity price forecasting. IEEE Trans. Power Syst. 33(1), 545–556 (2018)
Luo, J., Hong, T., Fang, S.-C.: Benchmarking robustness of load forecasting models under data integrity attacks. Int. J. Forecast. 34, 89–104 (2018)
Dong, G., Chen, Z.: Data driven energy management in a home microgrid based on Bayesian optimal algorithm. IEEE Trans. Ind. Inform
Fausett, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Pearson Education, Delhi (2006)
Shekhar, S., Amin, M.B.: Generalization by neural networks. IEEE Trans. Knowl. Data Eng. 4, 177–185 (1992)
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Abbasi, R.A., Javaid, N., Ghuman, M.N.J., Khan, Z.A., Ur Rehman, S., Amanullah (2019). Short Term Load Forecasting Using XGBoost. 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_108
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DOI: https://doi.org/10.1007/978-3-030-15035-8_108
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