Smart grid power load type forecasting: research on optimization methods of deep learning models

被引:1
|
作者
Sun, Huadong [1 ]
Ren, Yonghao [2 ]
Wang, Shanshan [2 ]
Zhao, Bing [2 ]
Yin, Rui [2 ]
机构
[1] State Key Lab Power Grid Safety & Energy Conservat, Beijing, Peoples R China
[2] China Elect Power Res Inst Co Ltd, Beijing, Peoples R China
关键词
smart grid; deep learning; optimization of intelligent systems; electric load type prediction; multi-source data; data analysis; SYSTEMS;
D O I
10.3389/fenrg.2023.1321459
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Optimization and research of smart grid load forecasting model based on deep learning
    Zhang, Dong
    INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES, 2024, 19 : 594 - 602
  • [2] Differentially Private Deep Learning for Load Forecasting on Smart Grid
    Soykan, Elif Ustundag
    Bilgin, Zeki
    Ersoy, Mehmet Aldf
    Tomur, Emrah
    2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2019,
  • [3] A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
    Aslam, Sheraz
    Herodotou, Herodotos
    Mohsin, Syed Muhammad
    Javaid, Nadeem
    Ashraf, Nouman
    Aslam, Shahzad
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2021, 144 (144):
  • [4] Assessing deep learning performance in power demand forecasting for smart grid
    Liang, Hengshuo
    Qian, Cheng
    Yu, Wei
    Griffith, David
    Golmie, Nada
    INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2024, 44 (01) : 36 - 48
  • [5] Short Term Load Forecasting based on Deep Learning for Smart Grid Applications
    Hafeez, Ghulam
    Javaid, Nadeem
    Ullah, Safeer
    Iqbal, Zafar
    Khan, Mahnoor
    Rehman, Aziz Ur
    Ziaullah
    INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2018, 2019, 773 : 276 - 288
  • [6] Enhancing smart grid reliability with advanced load forecasting using deep learning
    Jasmine, J.
    Nisha, M. Germin
    Prasad, Rajesh
    ELECTRICAL ENGINEERING, 2025,
  • [7] An Integrated Model of Deep Learning and Heuristic Algorithm for Load Forecasting in Smart Grid
    Alghamdi, Hisham
    Hafeez, Ghulam
    Ali, Sajjad
    Ullah, Safeer
    Khan, Muhammad Iftikhar
    Murawwat, Sadia
    Hua, Lyu-Guang
    MATHEMATICS, 2023, 11 (21)
  • [8] Hourly Electricity Load Forecasting in Smart Grid Using Deep Learning Techniques
    Khan, Abdul Basit Majeed
    Javaid, Nadeem
    Nazeer, Orooj
    Zahid, Maheen
    Akbar, Mariam
    Khan, Majid Hameed
    INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2019, 2020, 994 : 185 - 196
  • [9] A COMPREHENSIVE LEARNING-BASED MODEL FOR POWER LOAD FORECASTING IN SMART GRID
    Li, Huifang
    Li, Yidong
    Dong, Hairong
    COMPUTING AND INFORMATICS, 2017, 36 (02) : 470 - 492
  • [10] Photovoltaic Power Forecasting Methods in Smart Power Grid
    Yadav, Harendra Kumar
    Pal, Yash
    Tripathi, M. M.
    2015 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2015,