Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

Irfan, M.; Raza, Ali; Althobiani, Faisal; Ayub, Nasir; Idrees, Muhammad; Ali, Zain; Rizwan, Kashif; Alwadie, Abdullah; Ghonaim, Saleh Mohammed; Abdushkour, Hesham; Rahman, Saifur; AlShorman, Omar; Alqhtani, Samar M.

doi:10.32604/cmc.2022.025863

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…For the medium-and long-term and short-term prediction methods of electricity price, it is necessary to adopt different prediction models to analyze them, among which the commonly used methods include exponential smoothing method, ARMA model, neural network, etc., and then the relevant structure diagram can be constructed according to the electricity price prediction situation of big data analysis [2][3][4], As shown in Figure Figure 1.…”

Section: Methods Of Electricity Price Risk Prediction Based On Big Da...mentioning

confidence: 99%

Research on Electricity Price Risk Prediction and Settlement Monitoring Data System Based on Big Data Analysis in the Spot Market Environment

Xue,

Wang,

et al. 2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

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Compared with the uncertainty in the production and use of electricity, the fluctuation of electricity spot market price is greater, and it is very difficult to master. Moreover, there is no effective way to predict the price risk, and then the price risk prediction and settlement monitoring have a greater correlation with the relevant participants in the market. At the same time, the reliable electricity price risk prediction and settlement monitoring data system is the basis of spot market operation and not only price risk control but also unbalanced settlement risk . Therefore, the study on electricity price risk prediction and settlement monitoring data system based on big data analysis in the spot market environment, firstly summarizes the method of electricity price risk prediction based on big data analysis, highlighting the main content of the study; secondly, expounds a electricity price risk prediction system based on big data analysis in the spot environment, so as to complete the study.

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Section: Methods Of Electricity Price Risk Prediction Based On Big Da...mentioning

confidence: 99%

Research on Electricity Price Risk Prediction and Settlement Monitoring Data System Based on Big Data Analysis in the Spot Market Environment

Xue,

Wang,

et al. 2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

View full text Add to dashboard Cite

show abstract

“…A large number of studies have shown that the power load has self-similarity and long-term dependence [19,32]. At present, the power load comes from a variety of sources, and is susceptible to external factors such as weather and economy [33,34]. As a result, the increase in non-linearity and instability in power load data makes it challenging to accurately predict power loads [35].…”

Section: Related Workmentioning

confidence: 99%

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

Tian

Chen

et al. 2023

IET Generation Trans & Dist

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Accurate power load prediction is an important guide for power system planning and operation. High‐ or low‐load prediction results will affect the operation of the power system. In recent years, deep learning technology represented by convolution neural network (CNN) and transformer has been proved to be suitable for power load prediction. This paper proposes a new short‐term power load hybrid forecasting model, called channel enhanced attention (CEA) and temporal convolutional network (TCN)‐based transformer comprehensive forecasting model. This method combines the short‐term feature extraction ability of TCN with the long‐term dependent capture ability of transformer for short‐term load forecasting. And the CEA designed in this study is added to improve the prediction accuracy. On the same dataset, the designed model predicts power load mean square errors of 0.056 and 0.146 for the next 24 h and the next week, respectively, which is 0.002 to 0.073 and 0.012 to 0.024 lower than the baseline model. The experimental results show that the hybrid short‐term power load prediction model proposed in this paper is significantly better than the existing methods. The predicted curve is in agreement with the actual charge change, which provides a good guidance for short‐term power load prediction.

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“…Taheri et al [9] investigated long short-term memory (LSTM) to propose a short, mid, and long-term load forecasting model. Irfan et al [10] developed a DensetNet-121 based week-ahead load forecasting model with a support vector machine (SVM) ensemble to contribute an integration approach for combining multiple networks. Undoubtfully, the above ventures provide a thoughtful insight for electric load forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

Ali¹,

Riaz²,

Safoora³

et al. 2023

Computers, Materials &Amp; Continua

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Short-term load forecasting (STLF) is part and parcel of the efficient working of power grid stations. Accurate forecasts help to detect the fault and enhance grid reliability for organizing sufficient energy transactions. STLF ranges from an hour ahead prediction to a day ahead prediction. Various electric load forecasting methods have been used in literature for electricity generation planning to meet future load demand. A perfect balance regarding generation and utilization is still lacking to avoid extra generation and misusage of electric load. Therefore, this paper utilizes Levenberg-Marquardt (LM) based Artificial Neural Network (ANN) technique to forecast the short-term electricity load for smart grids in a much better, more precise, and more accurate manner. For proper load forecasting, we take the most critical weather parameters along with historical load data in the form of time series grouped into seasons, i.e., winter and summer. Further, the presented model deals with each season's load data by splitting it into weekdays and weekends. The historical load data of three years have been used to forecast week-ahead and day-ahead load demand after every thirty minutes making load forecast for a very short period. The proposed model is optimized using the Levenberg-Marquardt backpropagation algorithm to achieve results with comparable statistics. Mean Absolute Percent Error (MAPE), Root Mean Squared Error (RMSE), R 2 , and R are used to evaluate the model. Compared with other recent machine learning-based mechanisms, our model presents the best experimental results with MAPE and R 2 scores of 1.3 and 0.99, respectively. The results prove that the proposed LM-based ANN model performs much better in accuracy and has the lowest error rates as compared to existing work.

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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

Cited by 6 publications

References 23 publications

Research on Electricity Price Risk Prediction and Settlement Monitoring Data System Based on Big Data Analysis in the Spot Market Environment

Research on Electricity Price Risk Prediction and Settlement Monitoring Data System Based on Big Data Analysis in the Spot Market Environment

A hybrid prediction method for short‐term load based on temporal convolutional networks and attentional mechanisms

A Levenberg–Marquardt Based Neural Network for Short-Term Load Forecasting

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