Electricity Load and Price Forecasting Using Enhanced Machine Learning Techniques

Author:

Bano Hamida,Tahir Aroosa,Ali Ishtiaq,Khan Raja Jalees ul Hussen,Haseeb Abdul,Javaid Nadeem

Publisher

Springer International Publishing

Reference27 articles.

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)

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)

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)

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)

5. Raviv, E., Bouwman, K.E., van Dijk, D.: Forecasting day-ahead electricity prices: utilizing hourly prices. Energy Econ. 50, 227–239 (2015)

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