Deep Learning-Based Hybrid Model for Forecasting Locational Marginal Prices

Jiang, Zhisen; Wang, Jianxiao; Zhang, Tiance; Li, Gengyin; Zhou, Ming

doi:10.1109/icpsasia48933.2020.9208418

Cited by 2 publications

(1 citation statement)

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“…The authors in [8] combined extreme learning machine and PSO for interval prediction of wind power. The authors in [9] combine wavelet neural network and improved artificial bee colony for wind power interval prediction. The authors in [10] used support vector machine to predict the upper and lower bounds of electricity price and used PSO to optimize the hyper parameters of support vector machine.…”

Section: Introductionmentioning

confidence: 99%

Day Ahead Electricity Price Forecasting Based on the Deep Belief Network

Cao¹,

Wang

Liu³

et al. 2022

Wireless Communications and Mobile Computing

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With the reform of electric power system, major progress has been made in the construction of the electricity market. Electricity prices are a key influencing factor in the electricity market, and each participant trades electricity based on the price of electricity. Therefore, improving the accuracy of electricity price forecasts is important for every player in the electricity market. Prediction using single-layer neural networks has limited accuracy. Due to the high accuracy of machine learning in forecasting, the method of deep belief network is used to predict the price of electricity in the future. Real data from the U.S. PJM electricity market are used for simulation and compared with the prediction models of other neural networks. The results show that the prediction accuracy of the deep belief network model is higher, and the use of the deep belief network can provide an effective method for China’s electricity sales companies to predict electricity prices.

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Section: Introductionmentioning

confidence: 99%