Abstract
Conventional grid moves towards Smart Grid (SG). In conventional grids, electricity is wasted in generation-transmissions-distribution, and communication is in one direction only. SG is introduced to solve prior issues. In SG, there are no restrictions, and communication is bi-directional. Electricity forecasting plays a significant role in SG to enhance operational cost and efficient management. Load and price forecasting gives future trends. In literature many data-driven methods have been discussed for price and load forecasting. The objective of this paper is to focus on literature related to price and load forecasting in last four years. The author classifies each paper in terms of its problems and solutions. Additionally, the comparison of each proposed technique regarding performance are presented in this paper. Lastly, papers limitations and future challenges are discussed.
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Arif, A., Javaid, N., Anwar, M., Naeem, A., Gul, H., Fareed, S. (2020). Electricity Load and Price Forecasting Using Machine Learning Algorithms in Smart Grid: A Survey. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_43
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