Forecasting day-ahead electricity prices using a new integrated model

Zhang, Jinliang; Zhang, Yuejun; Li, Dezhi; Tan, Zhongfu; Ji, Jianfei

doi:10.1016/j.ijepes.2018.08.025

Cited by 62 publications

(25 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most commonly preferred decomposition method is the wavelet transform [113][114][115][116][117][118][119][120][121][122]. Other decomposition studies that used empirical mode are given in [123][124][125][126][127][128][129]. The most widely preferred feature selection methods are the correlation analysis are presented in [118,123,[130][131][132], and the mutual information method in [121,123,130,[133][134][135].…”

Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 99%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

View full text Add to dashboard Cite

Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

show abstract

Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 99%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

View full text Add to dashboard Cite

show abstract

“…Mid‐to‐long‐term forecasts 10 relate to annual and monthly 11 electricity price forecasts. Short‐term forecasts are weekly, daily, 12 or hourly electricity price forecasts 13 . Short‐term electricity price prediction has attracted particular attention in recent years because it can provide guidance for short‐term bidding strategies for participants in competitive markets, enabling them to formulate reasonable schemes and avoid risks while maximizing benefits.…”

Section: The Background Of Electricity Price Forecastingmentioning

confidence: 99%

A novel hybrid deep neural network model for short‐term electricity price forecasting

et al. 2020

View full text Add to dashboard Cite

Summary A Ubiquitous Power Internet of Things is fundamentally an Internet of Things, but focused upon power systems. Being able to predict these prices accurately may help with the identification of customer needs and the effective regulation of the power grid by power producers. It may also help electric power traders to manage risks, make correct decisions, and obtain more benefits. In this paper, a novel hybrid model is proposed for short‐term electricity price prediction. The model consists of three algorithms: Variational Mode Decomposition (VMD); a Convolutional Neural Network (CNN); and Gated Recurrent Unit (GRU). This is called SEPNet for convenience. The annual electricity price data is divided into seasons because of seasonal differences in the time series of electricity prices. The VMD algorithm is used to decompose the complex time series of electricity prices into intrinsic mode functions (IMFs) with different center frequencies. The CNN is used to further extract the time‐domain features for all the intrinsic model functions in the VMD domain. The GRU is then employed to process and learn the time‐domain features extracted by the CNN, leading to the final prediction. A comparison is made with five models, such as LSTM, CNN, VMD‐CNN, BP, VMD‐ELMAN. The results showed that the proposed model had the best performance, and it was found that using VMD can improve the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for the four seasons by 84% and 81%, respectively. The addition of GRU in the SEPNet model further improved the MAPE and RMSE by 19% and 25%, respectively. Including CNN and VMD‐CNN, that shows that the proposed model has the best performance. The MAPE and RMSE for the four seasonal averages are 0.730% and 0.453, respectively. This confirms that the SEPNet model has the feasibility and high accuracy to predict short‐term electricity prices.

show abstract

“…The energy liberalization in several countries in the world dissolved a unified market and converted it into a deregulated competitive one. The electric power market got considerable interest from research over the last few years [1]- [3]. Proposing accurate forecasting methods of the electricity price (EP) is very difficult due to the unique electric price features for instance highfrequency, non-linearity performance and seasonality, climatic variables, high volatility, a high percentage of unusual prices, plus the influence of renewable energy resources [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

2021

View full text Add to dashboard Cite

Forecasting day-ahead electricity prices using a new integrated model

Cited by 62 publications

References 48 publications

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

A novel hybrid deep neural network model for short‐term electricity price forecasting

Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

Contact Info

Product

Resources

About