Abstract
The evolution of electricity markets has led to increasingly complex energy trading dynamics and the integration of renewable energy sources as well as the influence of several external market factors contributed towards price volatility. Therefore, day-ahead electricity price forecasting models, typically using some kind of neural network, play a crucial role in the optimal behavior of market agents. The most prominent models and benchmarks rely on improving the accuracy of predictions and the time for convergence by some sort of a priori processing of the dataset that is used for the training of the neural network, such as hyperparameter tuning and feature selection techniques. What has been overlooked so far is the possible benefit of a posteriori processing, which would consider the effects of parameters that could refine the predictions once they have been made. Such a parameter is the estimation of the residual training error. In this study, we investigate the effect of residual training error estimation for the day-ahead price forecasting task and propose an error compensation deep neural network model (ERC–DNN) that focuses on the minimization of prediction error, while reinforcing error stability through the integration of an autoregression module. The experiments on the Nord Pool power market indicated that this approach yields improved error metrics when compared to the baseline deep learning structure in different training scenarios, and the refined predictions for each hourly sequence shared a more stable error profile. The proposed method contributes towards the development of more flexible hybrid neural network models and the potential integration of the error estimation module in future benchmarks, given a small and interpretable set of hyperparameters.
Highlights
Modern energy markets follow increasingly complex processes in order to perform efficient electricity trading that balances supply and demand while reacting to the dynamics derived from the unique characteristics and challenges of each energy system
We identified these research gaps and developed a hybrid error compensation deep neural network model, the ERC– DNN, which utilizes a feed-forward deep neural network for day-ahead electricity price predictions, as well as an autoregression module, which operates on the hourly residual error sequences and performs a step-by-step error estimation to refine the predicted prices
We present the results of the experiments with the inclusion of figures featuring a comparison of error metrics between the error compensation deep neural network model (ERC–DNN) and the baseline DNN for each training scenario
Summary
Modern energy markets follow increasingly complex processes in order to perform efficient electricity trading that balances supply and demand while reacting to the dynamics derived from the unique characteristics and challenges of each energy system. One of the main challenges that urge the development of more sophisticated techniques for the coordinated production and supply of electricity is price volatility [1]. Some of the most notable factors that could cause price fluctuations to include seasonal trends [2], weather conditions [3], penetration of renewable energy sources [4], challenges involving economic growth and changes in fuel cost [5], supply availability [6] and neighboring market dynamics [7]. Electricity price fluctuations could depend on the availability of fossil fuels for sufficient generation. The impact of neighboring markets on price volatility could be attributed to the increased player participation and decentralization that increases the complexity of price formation
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