Optimal Short Term Power Load Forecasting Algorithm by Using Improved Artificial Intelligence Technique

Waheed, Waqar; Xu, Qingshan

doi:10.1109/iccis49240.2020.9257675

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…This study proposes an SVR-based electricity demand forecasting method and contributes to exploring the factors influencing regional electricity demand using practical data. Compared with the SVR-based forecasting literature (e.g., Mei et al [38]; VanDeventer et al [42]; Waheed et al [16]), this paper newly incorporates the key factors influencing the power systems and economic development. One extension of this study would be to incorporate other machine-learning techniques to empower the forecasting framework.…”

Section: Discussionmentioning

confidence: 99%

“…Ensemble methods, optimization algorithms, time-series decomposition, and weather clustering were identified as important techniques, which could be used to enhance forecasting performance [15]. Shi et al [16] used an ensemble learning method combined with multiple predictors for electricity load forecasting, which had high prediction accuracy in dealing with complex multi-source problems.…”

Section: Power Forecasting Methodsmentioning

confidence: 99%

“…In this paper, we processed the collected time-series dataset to build the training data for predictive models. Following Waheed et al [16], we used the linear regression model, support vector regression model, and random forest regression model for predicting the regional power demand. Regarding the support vector regression model, we used the grid search method for optimizing the model parameters.…”

Section: Parameter Settingmentioning

confidence: 99%

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Machine-Learning-Based Electric Power Forecasting

Chen

Wang

et al. 2023

Sustainability

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The regional demand for electric power is influenced by a variety of factors, such as fluctuations in business cycles, dynamic linkages among regional development, and climate change. The valid quantification of the impacts of these factors on the demand for electric power poses significant challenges. Existing methods often fall short of capturing the inherent complexities. This paper addresses these limitations by proposing a framework, which integrates machine-learning techniques into regional electricity demand forecasting. Regional electricity generation firms could then leverage the power of machine learning and improve the accuracy and robustness of electric power forecasting. In this paper, we conduct extensive numerical experiments using an actual dataset from a large utility firm and other public data sources. The analysis indicates that the support vector regression model (the SVR model) has high accuracy in predicting the demand. The results show that socio-economic development is the major driver of growth in electricity demand, while weather variability is a key contributor to the seasonal fluctuations in electricity use. Furthermore, linkages among regional development and the status of development of the green economy become increasingly important influencing factors. The proposed forecasting approach helps the regional electricity generation firms reduce a large amount of carbon dioxide emissions.

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Section: Discussionmentioning

confidence: 99%

Section: Power Forecasting Methodsmentioning

confidence: 99%

Section: Parameter Settingmentioning

confidence: 99%

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Machine-Learning-Based Electric Power Forecasting

Chen

Wang

et al. 2023

Sustainability

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“…Short-term electrical demand forecasting powered by AI is crucial for optimizing energy generation and distribution, allocating resources effectively and maintaining power network stability [14]. AI-based models, including traditional machine learning (ML) and deep learning (DL) models, were introduced to handle non-linear relationships in load consumption.…”

Section: Load Forecasting Modelsmentioning

confidence: 99%

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Shiwakoti,

Charoenlarpnopparut,

Chapagain

2024

Applied Sciences

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Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry’s volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model’s validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.

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