How do machines predict energy use? Comparing machine learning approaches for modeling household energy demand in the United States

Burnett, J. Wesley; Kiesling, Lynne

doi:10.1016/j.erss.2022.102715

Cited by 6 publications

References 44 publications

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Forecasting the Spot Market Electricity Price with a Long Short-Term Memory Model Architecture in a Disruptive Economic and Geopolitical Context

Bâra,

Oprea,

Băroiu

2023

Int J Comput Intell Syst

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In this paper, we perform a short-run Electricity Price Forecast (EPF) with a Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM), using an algorithm that selects the variables and optimizes the hyperparameters. The results are compared with one of the standout machine learning algorithms, namely eXtreme Gradient Boosting (XGB). Apart from other EPF solutions, in this paper, we focus on the interval before and after the pandemic and the conflict in Ukraine. Furthermore, compared to the previous papers that mainly approached German, Austrian, Australian, Spanish, Nordic electricity Day Ahead Markets (DAM), we emphasize on the EPF for one of the East-European countries—Romania whose market rules closely align with the rules of the European Union electricity DAM. The contribution of this study consists in creating a data set that spans from January 2019 to August 2022 and providing an algorithm to identify the best stacked LSTM architecture to cope with a challenging short-term EPF. The proposed algorithm identifies the most relevant variables using a correlation threshold and performs a combination of three parameters—hidden layer size, dropout and learning rate generating the best EPF results.

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