Day-ahead electricity price forecasting via the application of artificial neural network based models

By I. Panapakidis, A. Dagoumas. 2016

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Incentive-based demand response for smart grid with reinforcement learning and deep neural network (2019, methodology): “Compared with the forecasting results in [44,58,59], the MAEs and MAPEs in this work are lower, indicating the DNN models in this paper can make reasonable and accurate price and load predictions.”
Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2020, background): “…this issue, it is important that any future research in EPF ensures that: (1) the split between the datasets is correctly described; (2) the test dataset does never overlap with the training or validation datasets; (3) the test dataset is always selected as the last segment of the full dataset.”
Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network (2019, methodology): “In this paper, the inputs of ANN are chosen based on correlation analysis, some empirical guidelines described in [28]–[30], and a number of accuracy tests and comparisons in the simulation.”
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, background): “3 2 [1, 3, 6, 12, 24, 48] 3 [1, 2, 4, 8, 16, 32] 4 [1, 3, 6, 12, 24]”