Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network

Lee, Chun‐Yao; Wu, Chang-En

doi:10.3390/en13174408

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The findings regarding the regression model specification variables and their implications can be tested for their validity and robustness in the broadest context of the newest and emerging advanced methodological approaches, data mining and data developments from various sources with an increasing role of artificial intelligence based on self-adaptive decomposition and heterogeneous ensemble learning [136], neural networks [137][138][139], and using artificial neural network-based customer profiles in smart grids [140]. Among issues for research in the future can also be to investigate the most recent and emerging challenging subjects, such as impacts on electricity consumption and the market pricing of energy in relation to renewable sources of energy [141][142][143] and various exogenous shocks such as ancillary services during the pandemic of the COVID-19 crisis [144].…”

Section: Discussionmentioning

confidence: 99%

Electricity Markets during the Liberalization: The Case of a European Union Country

Bojnec

Križaj

2021

Energies

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This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

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Section: Discussionmentioning

confidence: 99%

Electricity Markets during the Liberalization: The Case of a European Union Country

Bojnec

Križaj

2021

Energies

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“…A novel gene mapping strategy was encoded to search space, crossover, mutation, and selection to eliminate the unsatisfying candidates of validation fitness function. Four different distance models were used to determine the similarity among the reference forecast day and then price is forecasted through regression and artificial neural network techniques [16]. It is shown there is an improvement in accuracy using Pearson correlation coefficient model when the 45 framework days and 3 similar days are selected.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Price Forecasting Using EEMD and GRU-NN

Chughatta,

Haroon

2023

IJANSER

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A vital necessity for the reliable operation of the power system as well as for the financial well-being of the consumers is an accurate forecast of the price of electricity. The price of electricity is highly volatile, nonlinear, and subject to seasonal fluctuations. The price series is also affected by electrical market uncertainty and demand, which is strongly reliant on weather and electricity usage time. This study presents a short-term electricity price forecasting method that follows the similar days approach comprising Ensemble Empirical Mode Decomposition technique. The suggested method utilizes a feature selection methodology that combines the Random Forest Regressor and the Gradient Boosting Regressor to determine which features are most important for the machine learning model. To forecast the electricity price, a Gated Recurrent Unit Neural Network (GRU-NN) is employed as the machine learning model. The GRU-NN has the capability of accurately capturing complicated temporal relationships in electricity price time series, which enables it to make correct predictions. To evaluate the validity of the proposed method, data from the PJM electricity market have been used. The simulation results demonstrate that the suggested method is superior to the existing technique, with significantly improved values achieved for both the mean absolute percentage error (MAPE) and the root mean square error (RMSE).

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“…The forecasting process was then implemented after the generalized regression neural network (GRNN) parameters were optimized by the GSA. In contrast to previous works, Lee and Wu [12] proposed a similar day approach to predict electricity prices in the PJM energy market. The days were selected by four distance models: Euclidean norm, Manhattan distance, cosine coefficient, and Pearson correlation coefficient.…”

Section: Introductionmentioning

confidence: 99%

Next-Hour Electricity Price Forecasting Using Least Squares Support Vector Machine and Genetic Algorithm

Razak

Abidin

Yap

et al. 2022

AEJ

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Predicting the price of electricity is crucial for the operation of power systems. Short-term electricity price forecasting deals with forecasts from an hour to a day ahead. Hourly-ahead forecasts offer expected prices to market participants before operation hours. This is especially useful for effective bidding strategies where the bidding amount can be reviewed or changed before the operation hours. Nevertheless, many existing models have relatively low prediction accuracy. Furthermore, single prediction models are typically less accurate for different scenarios. Thus, a hybrid model comprising least squares support vector machine (LSSVM) and genetic algorithm (GA) was developed in this work to predict electricity prices with higher accuracy. This model was tested on the Ontario electricity market. The inputs, which were the hourly Ontario electricity price (HOEP) and demand for the previous seven days, as well as 1-h pre-dispatch price (PDP), were optimized by GA to prevent losing potentially important inputs. At the same time, the LSSVM parameters were optimized by GA to obtain accurate forecasts. The hybrid LSSVM-GA model was shown to produce an average mean absolute percentage error (MAPE) of 8.13% and the structure of this model is less complex compared with other models developed in previous studies. This is due to the fact that only two algorithms were used (LSSVM and GA), with the load and HOEP for the week preceding the forecasting hour as the inputs. Based on the results, it is concluded that the proposed hybrid algorithm is a promising alternative to produce good electricity price forecasts.

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Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network

Cited by 8 publications

References 26 publications

Electricity Markets during the Liberalization: The Case of a European Union Country

Electricity Markets during the Liberalization: The Case of a European Union Country

Short-Term Electricity Price Forecasting Using EEMD and GRU-NN

Next-Hour Electricity Price Forecasting Using Least Squares Support Vector Machine and Genetic Algorithm

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