Day-ahead Electricity Price Forecasting Using LSTM Networks

Miletić, Marija; Pavić, Ivan; Pandžić, Hrvoje; Capuder, Tomislav

doi:10.23919/splitech55088.2022.9854351

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the combination of intelligence optimization theories and advancements in computer technology has led to a growing research interest in predictive models in this area, particularly using deep learning (DL) architectures due to their remarkable performance and broad application scope. There have been developments in electricity price time series prediction models with different architectures, some of which are: convolutional network (CNN) [51][52][53], recurrent neural network (RNN)-based models [54][55][56][57][58], generative models [59,60], Bayesian networks (BNs) [61,62], and hybrid models (ensembles, signal preprocessing steps, among others) [63][64][65][66].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results (using MAPE, RMSE, and variance) show in both cases the accuracy of the load and price predictions. Similarly, in [58], electricity price forecasting is explored, focusing on proposed long-term and shortterm memory networks using historical prices, timestamps, and additional engineering features. The research results highlight the meaningful impact of feature selection on forecast accuracy and the importance of selecting appropriate test datasets, especially when drastic trend changes occur in historical data.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

View full text Add to dashboard Cite

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…Among them, LSTM has better accuracy in electricity price prediction [12]. To capture the relationship between historical electricity price timestamps, the LSTM network was proposed by Miletić et al [13]. The CNN-LSTM was proposed by Bao et al [14].…”

Section: Related Workmentioning

confidence: 99%

Day-Ahead Electricity Price Forecasting Based on GCM Filtering and Higher-Order Pooling Feature Enhancement

Sun,

Wang,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Redundant complexities and inadequate representation of spatiotemporal features are included in the electricity price data. To address complex data redundancy and inadequate representation of spatiotemporal features, a gated channel mechanism (GCM) combined with a high-order pooling feature enhanced convolutional LSTM network (GCHCon-LSTM) electricity price prediction model is proposed. On the standardized processed electricity price dataset, the data vertical correlation information is expanded using gated dual convolutional neural network (GDCNN) integrated adjustment features. Redundant features are filtered using GCM. Temporal and spatial features are extracted by LSTM and ASPConv. Key information on spatial features is extracted using higher-order pooling. Combined with temporal features, the spatiotemporal feature representation is enhanced in a time-dominant and spatial manner. The prediction result is obtained by error correction. On the ERCOT Houston area electricity price dataset, compared to LSTM, CNN-LSTM, GHTnet and ILRCN, the experimental results showed that MAE, MSE and RMSE are reduced by the highest 21.50%, 29.56% and 40.18%, respectively, and the lowest 7.95%, 6.39% and 13.60%.INDEX TERMS Electricity price forecasting, GDCNN, Gated channel mechanism, Higher-order pooling enhancement, LSTM

show abstract

Temporal Convolutional Network Ensemble for Day-Ahead and Week-Ahead Electricity Price Forecasting

Lee,

Mandal

2023

2023 IEEE Industry Applications Society Annual Meeting (IAS)

View full text Add to dashboard Cite

Day-ahead Electricity Price Forecasting Using LSTM Networks

Cited by 4 publications

References 16 publications

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Day-Ahead Electricity Price Forecasting Based on GCM Filtering and Higher-Order Pooling Feature Enhancement

Temporal Convolutional Network Ensemble for Day-Ahead and Week-Ahead Electricity Price Forecasting

Contact Info

Product

Resources

About