Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

被引:31
作者
Moradzadeh, Arash [1 ]
Moayyed, Hamed [2 ]
Zakeri, Sahar [1 ]
Mohammadi-Ivatloo, Behnam [1 ]
Aguiar, A. Pedro [2 ]
机构
[1] Univ Tabriz, Fac Elect & Comp Engn, Tabriz 516615731, Iran
[2] Univ Porto, Dept Elect & Comp Engn, P-4000008 Porto, Portugal
关键词
energy management; microgrid; residential and commercial loads; short-term load forecasting; deep learning; bidirectional long short-term memory (Bi-LSTM); SUPPORT VECTOR REGRESSION; NEURAL-NETWORKS; POWER; HYBRID; OPTIMIZATION; PERFORMANCE; ALGORITHMS; EXTRACTION; SYSTEMS; DESIGN;
D O I
10.3390/inventions6010015
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.
引用
收藏
页码:1 / 11
页数:11
相关论文
共 39 条
[1]   Multi-agent microgrid energy management based on deep learning forecaster [J].
Afrasiabi, Mousa ;
Mohammadi, Mohammad ;
Rastegar, Mohammad ;
Kargarian, Amin .
ENERGY, 2019, 186
[2]   Review of Fuel Cell Technologies and Applications for Sustainable Microgrid Systems [J].
Akinyele, Daniel ;
Olabode, Elijah ;
Amole, Abraham .
INVENTIONS, 2020, 5 (03) :1-35
[3]   Long short-term memory [J].
Hochreiter, S ;
Schmidhuber, J .
NEURAL COMPUTATION, 1997, 9 (08) :1735-1780
[4]   LEARNING LONG-TERM DEPENDENCIES WITH GRADIENT DESCENT IS DIFFICULT [J].
BENGIO, Y ;
SIMARD, P ;
FRASCONI, P .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1994, 5 (02) :157-166
[5]  
Caliano M, 2017, ENERG CONVERS MANAGE, V149, P631, DOI [10.1016/j.enconman.2017.07.048, 10.1016/J.enconman.2017.07.048]
[6]   Short-term load forecasting using a kernel-based support vector regression combination model [J].
Che, JinXing ;
Wang, JianZhou .
APPLIED ENERGY, 2014, 132 :602-609
[7]   Pre-Processing of Energy Demand Disaggregation Based Data Mining Techniques for Household Load Demand Forecasting [J].
Ebrahim, Ahmed F. ;
Mohammed, Osama A. .
INVENTIONS, 2018, 3 (03)
[8]   Tidal Supplementary Control Schemes-Based Load Frequency Regulation of a Fully Sustainable Marine Microgrid [J].
Fayek, Hady H. ;
Mohammadi-Ivatloo, Behnam .
INVENTIONS, 2020, 5 (04) :1-17
[9]   Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production [J].
Ferlito, S. ;
Adinolfi, G. ;
Graditi, G. .
APPLIED ENERGY, 2017, 205 :116-129
[10]   An Innovative Stochastic Multi-Agent-Based Energy Management Approach for Microgrids Considering Uncertainties [J].
Ghorbani, Sajad ;
Unland, Rainer ;
Shokouhandeh, Hassan ;
Kowalczyk, Ryszard .
INVENTIONS, 2019, 4 (03)