Forecasting the Spot Market Electricity Price with a Long Short-Term Memory Model Architecture in a Disruptive Economic and Geopolitical Context
Abstract 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 lear...
Main Authors: | Adela Bâra, Simona-Vasilica Oprea, Alexandru-Costin Băroiu |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2023-08-01
|
Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://doi.org/10.1007/s44196-023-00309-3 |
Similar Items
-
Predicting Day-Ahead Electricity Market Prices through the Integration of Macroeconomic Factors and Machine Learning Techniques
by: Adela Bâra, et al.
Published: (2024-01-01) -
Understanding electricity price evolution – day-ahead market competitiveness in Romania
by: Adela Bâra, et al.
Published: (2023-05-01) -
Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management
by: Simona-Vasilica Oprea, et al.
Published: (2022-09-01) -
XGBoost-Based Day-Ahead Load Forecasting Algorithm Considering Behind-the-Meter Solar PV Generation
by: Dong-Jin Bae, et al.
Published: (2021-12-01) -
Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles
by: Nadeem Javaid, et al.
Published: (2018-06-01)