An effective Two-Stage Electricity Price forecasting scheme

Shi, Wei; Wang, Yufeng; Chen, Yiyuan; Ma, Jun

doi:10.1016/j.epsr.2021.107416

Cited by 30 publications

(10 citation statements)

References 23 publications

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“…According to the description in the previous section, the formation of spot market electricity price is formed by the joint action of a variety of factors. This paper summarizes the research on existing electricity price forecasting factors as follows: historical electricity price, market demand, thermal power output, New energy output, provincial load adjustment and market player strategy [44][45][46].…”

Section: Identification Of Electricity Price Forecasting Factors In S...mentioning

confidence: 99%

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

et al. 2022

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With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

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Section: Identification Of Electricity Price Forecasting Factors In S...mentioning

confidence: 99%

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

et al. 2022

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“…• using so-called variance stabilizing transformations (VSTs; Schneider, 2011;Diaz and Planas, 2016;Narajewski and Ziel, 2020a;Shi et al, 2021) to make the marginal distributions less heavy-tailed (Box-Cox family, area hyperbolic sine) or Gaussian (Probability Integral Transform, see Section 5.1.2);…”

Section: Trend #2: From Regression To Statistical and Machine Learningmentioning

confidence: 99%

Forecasting Electricity Prices

Maciejowska¹,

Uniejewski²,

Weron³

2022

Preprint

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Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. It is interdisciplinary by nature. It requires expertise in econometrics, statistics or machine learning for developing well-performing predictive models, in finance for understanding market mechanics, and in electrical engineering for comprehension of the fundamentals driving electricity prices.Although electricity price forecasting aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure.On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

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“…Conventional electricity price prediction focuses on the price prediction from the perspectives of prediction horizons and various prediction models [8]. A two-stage electricity price forecast scheme is developed to predict electricity price spike in the first stage and continuous price in the second stage for improving prediction accuracy [9]. Hybrid models are developed to improve prediction accuracy based on wavelet and LSTM networks [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the contribution of this paper is threefold: 1) To the authors' best knowledge, this paper first takes advantage of both prediction error and decision error to learn the parameters of prediction models and proposes a decisionfocused electricity price prediction approach for ESS arbitrage. Compared with previous prediction methods [9]- [13], the proposed decision-focused approach pays attention to the prediction error's impact on the downstream optimization models, improving the decision accuracy under the predicted price.…”

Section: Introductionmentioning

confidence: 99%

Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-Focused Approach

Sang

Long

et al. 2022

IEEE Trans. Smart Grid

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Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.

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An effective Two-Stage Electricity Price forecasting scheme

Cited by 30 publications

References 23 publications

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

Forecasting Electricity Prices

Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-Focused Approach

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