Next-day electricity price forecasting on deregulated power market

Toyama, Hirofumi; Senjyu, Tomonobu; Areekul, Phatchakorn; Chakraborty, Shantanu; Yona, Atsushi; Funabashi, Toshihisa

doi:10.1109/td-asia.2009.5356988

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Connection outcomes demonstrate the connections in week after week variety statistics on electricity expenses. The %MAPE for the year in PJM market is found to be 11-33% [3].…”

Section: Introductionmentioning

confidence: 95%

Research on Various Electricity Price Forecasting Techniques for Indian Power Markets

2019

ijrte

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A stable market Clearing rate (MCP) guaging machine is required for the two customers and electricity creators for succesful and worthwhile strength promote execution. This evaluation work predicts the marketplace Clearing fees (MCPs) for the extensive stretches of April, may and June using artificial Neural Networks (ANNs). the obvious charges and needs are accumulated from the Indian strength alternate (IEX) are used as records sources and centers for ANNs in looking ahead to MCPs. The hours with closeness in fees are gathered reliant on affiliation framework and peak-Off peak estimations of fees. Neural Networks are executed autonomously for each social affair to expect MCPs correctly. mean Absolute percentage mistakes (MAPEs) are evaluated to discover the great assembling machine and foreseeing version for Indian power Markets. MAPE effects are confirmed up within the first-class innovative days and the complete month for showing the ampleness of these get-together strategies. the connection amassing system is included because it indicates low MAPE for all instances. This examination offers an estimation to Indian power Markets as for the guaging model to screen and spoil down the instability in strength fees of IEX. this may assist the controllers with defining continuously effective processes on deregulation and modifications when compelling gauge opposition for the duration of strength markets modifications.

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“…Connection outcomes demonstrate the connections in week after week variety statistics on electricity expenses. The %MAPE for the year in PJM market is found to be 11-33% [3].…”

Section: Introductionmentioning

confidence: 95%

Research on Various Electricity Price Forecasting Techniques for Indian Power Markets

2019

ijrte

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“…The main result in [12] forecasts SMP by considering oil and gas price as additional predictors in an SVM model. Another simulation proposes a one-day ahead of SMP forecasting and they increase the prediction accuracy especially during a peak time by utilizing a weekly variation of SMP data [13]. In order to enhance forecasting results, a modern technique like deep learning is also utilized for short-term SMP forecasting because of the well-known capability of deep learning to analyze non-linear data [14,15].…”

Section: Introductionmentioning

confidence: 99%

Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

2019

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Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.

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“…𝐷 𝑡,𝑑−1Electricity demand of hour 𝑡 on the day 𝑑 − 1[20],[29],[39],[42],[27] 𝐷 𝑡,𝑑 Forecasted electricity demand of hour 𝑡 on the day 𝑑[42] 𝑅𝑒 𝑡,𝑑−1 Renewable generation of hour 𝑡 on the day 𝑑 − 1[23] 𝑅𝑒 𝑡,𝑑 Forecasted renewable generation of hour 𝑡 on the day 𝑑[23],[42] …”

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confidence: 99%

Selection of input variables for electricity price forecast

Wei

2023

Preprint

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The selection of input variables is vital for performance of electricity price forecast model when artificial neural network (ANN) is used to forecast electricity price. However, the selection of input variables mainly is based on experience and heuristics in current studies. This paper aims to compare three categories methods, (1) the relevance between input variables and electricity price, (2) the relationship between different input variables and (3) the structure of ANN, on selecting input variables in terms of electricity price. In order to avoid influence of structure of ANN on performance of selecting input variables, the back-propagation (BP) network and Elman network that represent different structure of ANN respectively are used to forecast electricity price. The real electricity market data from Nordic and California electricity market is used to examine the performance of electricity price forecast model, the result shows that the electricity price forecast model formed using input variables selected by category, the relevance between input variables and electricity price, has the best performance of electricity price forecast.

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Next-day electricity price forecasting on deregulated power market

Cited by 9 publications

References 7 publications

Research on Various Electricity Price Forecasting Techniques for Indian Power Markets

Research on Various Electricity Price Forecasting Techniques for Indian Power Markets

Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

Selection of input variables for electricity price forecast

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