A Kernel-Based Real-Time Adaptive Dynamic Programming Method for Economic Household Energy Systems

Jun, Yuan; Chen, Sizhe; Yu, Samson Shenglong; Zhang, Guidong; Chen, Zhe; Zhang, Yun

doi:10.1109/tii.2022.3181034

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…Recently, due to the advantages of artificial intelligence methods in data forecasting and intelligent analysis, power system load forecasting based on machine learning (ML) Liao et al (2021); Yuan et al (2023) has gradually emerged. For example, the load forecast based on the time series model can be extended to a multiclass regression model to predict the power load by establishing a time series model for the grid power or the method based on the support vector machine (SVM) can use its own excellent binary classification characteristics.…”

Section: Motivationmentioning

confidence: 99%

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Intelligent grid load forecasting based on BERT network model in low-carbon economy

Tao

et al. 2023

Front. Energy Res.

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In recent years, the reduction of high carbon emissions has become a paramount objective for industries worldwide. In response, enterprises and industries are actively pursuing low-carbon transformations. Within this context, power systems have a pivotal role, as they are the primary drivers of national development. Efficient energy scheduling and utilization have therefore become critical concerns. The convergence of smart grid technology and artificial intelligence has propelled transformer load forecasting to the forefront of enterprise power demand management. Traditional forecasting methods relying on regression analysis and support vector machines are ill-equipped to handle the growing complexity and diversity of load forecasting requirements. This paper presents a BERT-based power load forecasting method that leverages natural language processing and image processing techniques to enhance the accuracy and efficiency of transformer load forecasting in smart grids. The proposed approach involves using BERT for data preprocessing, analysis, and feature extraction on long-term historical load data from power grid transformers. Multiple rounds of training and fine-tuning are then conducted on the BERT architecture using the preprocessed training datasets. Finally, the trained BERT model is used to predict the transformer load, and the predicted results are compared with those obtained based on long short-term memory (LSTM) and actual composite values. The experimental results show that compared with LSTM method, the BERT-based model has higher short-term power load prediction accuracy and feature extraction capability. Moreover, the proposed scheme enables high levels of accuracy, thereby providing valuable support for resource management in power dispatching departments and offering theoretical guidance for carbon reduction initiatives.

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Section: Motivationmentioning

confidence: 99%

“…Through technological innovation and cooperation and sharing, power companies can achieve more intelligent, efficient and sustainable power system resource management, and make greater contributions to carbon neutrality and carbon reduction actions Yuan et al (2023).…”

mentioning

confidence: 99%

Intelligent grid load forecasting based on BERT network model in low-carbon economy

Tao

et al. 2023

Front. Energy Res.

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“…This is particularly important in the field of energy forecasting, where the dynamism and complexity of the data require sophisticated analytical approaches. The Transformer architecture, with its advanced mechanisms for handling sequential data, provides a robust framework for capturing temporal dependencies and nuances, thereby enhancing the accuracy and reliability of predictive analysis in the energy sector [20].…”

Section: Introduction (Literature Review)mentioning

confidence: 99%

Deep Recurrent Neural Networks in Energy Demand Forecasting: A Case Study of Kazakhstan's Electrical Consumption

Kabdygali,

Omirgaliyev,

Tursynbayev

et al. 2024

sjaitu

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The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency.

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Demand Forecasting Mathematical Models for Residential Electricity Consumption Considering Ambient Temperature

Stanytsina,

Zaporozhets,

Artemchuk

2024

Studies in Systems, Decision and Control

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A Kernel-Based Real-Time Adaptive Dynamic Programming Method for Economic Household Energy Systems

Cited by 6 publications

References 30 publications

Intelligent grid load forecasting based on BERT network model in low-carbon economy

Intelligent grid load forecasting based on BERT network model in low-carbon economy

Deep Recurrent Neural Networks in Energy Demand Forecasting: A Case Study of Kazakhstan's Electrical Consumption

Demand Forecasting Mathematical Models for Residential Electricity Consumption Considering Ambient Temperature

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