Accuracy Analysis of Selected Time Series and Machine Learning Methods for Smart Cities based on Estonian Electricity Consumption Forecast

被引:3
|
作者
Haring, Tobias [1 ,2 ]
Ahmadiahangar, Roya [1 ,2 ]
Rosin, Argo [1 ,2 ]
Korotko, Tarmo [1 ,2 ]
Biechl, Helmuth [2 ,3 ]
机构
[1] Tallinn Univ Technol, Smart City Ctr Excellence Finest Twins, Tallinn, Estonia
[2] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, Tallinn, Estonia
[3] Univ Appl Sci Kempten, Inst Elect Power Syst IEES, Kempten, Germany
关键词
Time Series Analysis; Smart City; Machine Learning; Load Forecast; Load Prediction; Distribution Grid;
D O I
10.1109/CPE-POWERENG48600.2020.9161690
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
Increasing shares of renewable energy sources in combination with rising popularity of demand response applications and flexibility programs forces higher awareness for production and consumption balancing. Accurate models for forecasting are not just necessary for PV- or wind power sources in smart cities, but also the prediction of loads respectively consumption, which can be based on time series analysis or machine learning methods. Three of those methods, namely a linear regression (LM), a long short-term memory network (LSTM) and a neural network model (NN), have been selected to see their performance on predicting the load of a large smart city on the example of the Estonian electricity consumption data. Hourly data of the year 2019 was used as training data to predict the first 20 days of 2020. For this kind of prediction, the LM showed the lowest root mean square error (RMSE) and had the lowest computational time. The neural network was slightly less accurate. The LSTM showed the worst performance in terms of accuracy and computational time. Thus, LSTM is not the preferred method for this kind of prediction and the recommendation for forecasting such loads would be a LM because the RMSE and computational effort needed are lower than for a NN.
引用
收藏
页码:425 / 428
页数:4
相关论文
共 50 条
  • [1] Electricity consumption forecasting for sustainable smart cities using machine learning methods
    Peteleaza, Darius
    Matei, Alexandru
    Sorostinean, Radu
    Gellert, Arpad
    Fiore, Ugo
    Zamfirescu, Bala-Constantin
    Palmieri, Francesco
    [J]. INTERNET OF THINGS, 2024, 27
  • [2] Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners
    Chou, Jui-Sheng
    Hsu, Shu-Chien
    Ngoc-Tri Ngo
    Lin, Chih-Wei
    Tsui, Chia-Chi
    [J]. IEEE SYSTEMS JOURNAL, 2019, 13 (03): : 3120 - 3128
  • [3] Data analysis-based time series forecast for managing household electricity consumption
    Bezzar, Nour El-Houda
    Laimeche, Lakhdar
    Meraoumia, Abdallah
    Houam, Lotfi
    [J]. DEMONSTRATIO MATHEMATICA, 2022, 55 (01) : 900 - 921
  • [4] Forecasting Energy Consumption of Office Building by Time Series Analysis Methods based on Machine Learning Algorithm
    Liu, Dandan
    Yang, Qiangqiang
    Yang, Fang
    [J]. 2019 6TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE 2019), 2019, : 297 - 301
  • [5] Monitoring Electricity Consumption Based on Time Series Analysis
    Diaz Redondo, Rebeca P.
    Fernandez Vilas, Ana
    Estevez Caldas, Alberto
    [J]. INTELLIGENT ENVIRONMENTS 2020, 2020, 28 : 321 - 330
  • [6] Multivariate Extreme Learning Machine Based AutoEncoder for Electricity Consumption Series Clustering
    Zheng, Kaihong
    Yang, Jingfeng
    Gong, Qihang
    Zhou, Shangli
    Zeng, Lukun
    Li, Sheng
    [J]. IEEE ACCESS, 2021, 9 : 148665 - 148675
  • [7] Forecasting daily natural gas consumption with regression, time series and machine learning based methods
    Yucesan, Melih
    Pekel, Engin
    Celik, Erkan
    Gul, Muhammet
    Serin, Faruk
    [J]. ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2021,
  • [8] A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities
    Matos, Miguel
    Almeida, Joao
    Goncalves, Pedro
    Baldo, Fabiano
    Braz, Fernando Jose
    Bartolomeu, Paulo C.
    [J]. ENERGIES, 2024, 17 (03)
  • [9] Time series analysis sales of sowing crops based on machine learning methods
    Al-Gunaid, Mohammed A.
    Shcherbakov, Maxim, V
    Trubitsin, Vladislav V.
    Shumkin, Alexandr M.
    [J]. 2018 9TH INTERNATIONAL CONFERENCE ON INFORMATION, INTELLIGENCE, SYSTEMS AND APPLICATIONS (IISA), 2018, : 106 - 111
  • [10] BI-LSTM-LSTM Based Time Series Electricity Consumption Forecast for South Korea
    Gul, Malik Junaid Jami
    Firmansyah, M. Hafid
    Rho, Seungmin
    Paul, Anand
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE AND APPLIED COGNITIVE COMPUTING, 2021, : 897 - 902