Short Term Electricity Price Forecasting Through Convolutional Neural Network (CNN)

Khan, Zahoor Ali; Fareed, Sahiba; Anwar, Mubbashra; Naeem, Ayesha; Gul, Hira; Arif, Arooj; Javaid, Nadeem

doi:10.1007/978-3-030-44038-1_108

Cited by 17 publications

(6 citation statements)

References 17 publications

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“…VI. RELATED WORK While many ML models have been proposed for EPF [6], [11]- [21], only two papers have tried to include the pricefixing algorithm. First, Schürch and Wagner [28] aim to extract features from the ORDER BOOK before feeding them to a ML algorithm.…”

Section: Results A) Configurationsmentioning

confidence: 99%

“…EPF is complex as many factors influence it, both at the level of production and consumption. Different methods have been used so far, such as auto-regressive methods [5]- [10], but also augmented machine learning models for the EPF problem [6], [11]- [21]. The two approaches focus on finding relationships between exogenous features (electricity consumption and production forecasts) and price histories with day-ahead prices, and machine learning models have recently proven to be superior to auto-regressive models [22].…”

Section: Introductionmentioning

confidence: 99%

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Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Tschora,

Guns,

Pierre

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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We consider day-ahead electricity price forecasting on the European market. In this market, participants can offer electricity for sale or purchase for a specific price by submitting overnight orders. Market operators determine the market clearing price -the price at which the amount of electricity supplied equals the amount of electricity demanded -using the Euphemia balancing algorithm. EUPHEMIA is a quadratic optimization problem that maximizes the social welfare defined as the sum of the supplier surplus and consumer surplus while ensuring a null energy balance. This mechanism deeply influences the price calculation, but has so far been little considered in electricity price forecasting algorithms. Existing models are generally based on identifying relationships between exogenous characteristics (consumption and production forecasts) and the market clearing price to be predicted. A few studies have examined the EUPHEMIA mechanism during prediction, by doing costly manual transformations on order books. In this article, we overcome this limitation by considering the pricing mechanism during model training. For this, we use a predict-and-optimize strategy with differentiable optimization. We design a fully differentiable and scalable solving method for the EUPHEMIA optimization problem and apply it on real-life data from the European Power Exchange (EPEX). We design different model architectures using our differentiable solver and empirically study the impact of taking into account the optimal calculation of prices within the training of the neural network.

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Section: Results A) Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Tschora,

Guns,

Pierre

et al. 2023

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)

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“…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%

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

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

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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.

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“…Meng et al (2022) develop an attention mechanism long short-term memory (AT-LSTM) network, which enables the selection of important information in different situations and is more robust and flexible. Khan et al (2020) use a convolutional neural network (CNN) for feature selection in EPF, which achieves better performance than an artificial neural network (ANN). Hybrid models combine multiple individual models, which makes them more flexible and efficient.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic electricity price forecasting based on penalized temporal fusion transformer

Jiang,

Pan,

Dong

et al. 2024

Journal of Forecasting

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AbstactIn the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications.

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Short Term Electricity Price Forecasting Through Convolutional Neural Network (CNN)

Cited by 17 publications

References 17 publications

Electricity Price Forecasting based on Order Books: a differentiable optimization approach

Electricity Price Forecasting based on Order Books: a differentiable optimization approach

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

Probabilistic electricity price forecasting based on penalized temporal fusion transformer

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