{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:57:50Z","timestamp":1771516670250,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["project IMPROVE (PO-CI-01-0145-FEDER-031823), funded by the FEDER Funds through the COMPETE2020 - POCI, and in part by the National Funds (PIDDAC)."],"award-info":[{"award-number":["project IMPROVE (PO-CI-01-0145-FEDER-031823), funded by the FEDER Funds through the COMPETE2020 - POCI, and in part by the National Funds (PIDDAC)."]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Inventions"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/inventions6010015","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2557-1307","authenticated-orcid":false,"given":"Arash","family":"Moradzadeh","sequence":"first","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166\/15731, Iran"}]},{"given":"Hamed","family":"Moayyed","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Porto, 4000-008 Porto, Portugal"}]},{"given":"Sahar","family":"Zakeri","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166\/15731, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0255-8353","authenticated-orcid":false,"given":"Behnam","family":"Mohammadi-Ivatloo","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166\/15731, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7105-0505","authenticated-orcid":false,"given":"A. Pedro","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Porto, 4000-008 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"116256","DOI":"10.1016\/j.apenergy.2020.116256","article-title":"Impact of operating uncertainty on the performance of distributed, hybrid, renewable power plants","volume":"282","author":"Ihsan","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.enconman.2017.07.048","article-title":"Design optimization and sensitivity analysis of a biomass-fired combined cooling, heating and power system with thermal energy storage systems","volume":"149","author":"Caliano","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Akinyele, D., Olabode, E., and Amole, A. (2020). Review of fuel cell technologies and applications for sustainable microgrid systems. Inventions, 5.","DOI":"10.3390\/inventions5030042"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sadeghian, O., Moradzadeh, A., Mohammadi-Ivatloo, B., Abapour, M., and Marquez, F.P.G. (2020). Generation units maintenance in combined heat and power integrated systems using the mixed integer quadratic programming approach. Energies, 13.","DOI":"10.3390\/en13112840"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106901","DOI":"10.1016\/j.epsr.2020.106901","article-title":"Existing Developments in Adaptive Smart Grid Protection: A Review","volume":"191","author":"Khalid","year":"2021","journal-title":"Electric Power Syst. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106364","DOI":"10.1016\/j.epsr.2020.106364","article-title":"Markovian-based stochastic operation optimization of multiple distributed energy systems with renewables in a local energy community","volume":"186","author":"Yan","year":"2020","journal-title":"Electric Power Syst. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ghorbani, S., Unland, R., Shokouhandeh, H., and Kowalczyk, R. (2019). An innovative stochastic multi-agent-based energy management approach for microgrids considering uncertainties. Inventions, 4.","DOI":"10.3390\/inventions4030037"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fayek, H.H., and Mohammadi-Ivatloo, B. (2020). Tidal Supplementary Control Schemes-Based Load Frequency Regulation of a Fully Sustainable Marine Microgrid. Inventions, 5.","DOI":"10.3390\/inventions5040053"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khan, M., Khan, M., Jiang, H., Hashmi, K., and Shahid, M. (2018). An Improved Control Strategy for Three-Phase Power Inverters in Islanded AC Microgrids. Inventions, 3.","DOI":"10.3390\/inventions3030047"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, E.-K., Shi, W., Gadh, R., and Kim, W. (2016). Design and Implementation of a Microgrid Energy Management System. Sustainability, 8.","DOI":"10.3390\/su8111143"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117857","DOI":"10.1016\/j.energy.2020.117857","article-title":"Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform","volume":"203","author":"Tayab","year":"2020","journal-title":"Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.energy.2018.11.128","article-title":"A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing","volume":"168","author":"Zhang","year":"2019","journal-title":"Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.energy.2018.10.119","article-title":"Short term load forecasting based on feature extraction and improved general regression neural network model","volume":"166","author":"Liang","year":"2019","journal-title":"Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Moradzadeh, A., Zakeri, S., Shoaran, M., Mohammadi-Ivatloo, B., and Mohammadi, F. (2020). Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms. Sustainability, 12.","DOI":"10.3390\/su12177076"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.apenergy.2017.07.124","article-title":"Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production","volume":"205","author":"Ferlito","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113353","DOI":"10.1016\/j.apenergy.2019.113353","article-title":"A novel composite neural network based method for wind and solar power forecasting in microgrids","volume":"251","author":"Heydari","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103894","DOI":"10.1016\/j.engappai.2020.103894","article-title":"Artificial neural networks in microgrids: A review","volume":"95","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.apenergy.2014.07.064","article-title":"Short-term load forecasting using a kernel-based support vector regression combination model","volume":"132","author":"Che","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.energy.2019.01.075","article-title":"Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting","volume":"171","author":"Wen","year":"2019","journal-title":"Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.asoc.2013.12.001","article-title":"Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank","volume":"16","author":"Selakov","year":"2014","journal-title":"Appl. Soft Comput. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"862185","DOI":"10.1155\/2015\/862185","article-title":"Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm","volume":"2015","author":"Sun","year":"2015","journal-title":"J. Electrical Comput. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1049\/iet-rpg.2017.0867","article-title":"DNN-based approach for fault detection in a direct drive wind turbine","volume":"12","author":"Teng","year":"2018","journal-title":"IET Renew. Power Gener."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep Learning Algorithms for Bearing Fault Diagnostics\u2014A Comprehensive Review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Moradzadeh, A., and Pourhossein, K. (2019, January 27\u201329). Short Circuit Location in Transformer Winding Using Deep Learning of Its Frequency Responses. Proceedings of the 2019 International Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2019 and 2019 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2019, Istanbul, Turkey.","DOI":"10.1109\/ACEMP-OPTIM44294.2019.9007176"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"15796","DOI":"10.1109\/ACCESS.2021.3051411","article-title":"High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions","volume":"9","author":"Teimourzadeh","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3271","DOI":"10.1109\/TII.2018.2825243","article-title":"Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks","volume":"14","author":"Yu","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111799","DOI":"10.1016\/j.enconman.2019.111799","article-title":"A review of deep learning for renewable energy forecasting","volume":"198","author":"Wang","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/TSG.2019.2918330","article-title":"A practical solution for non-intrusive type II load monitoring based on deep learning and post-processing","volume":"11","author":"Kong","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Moradzadeh, A., Mohammadi-Ivatloo, B., Abapour, M., Anvari-Moghaddam, A., Gholami Farkoush, S., and Rhee, S.B. (2021). A practical solution based on convolutional neural network for non-intrusive load monitoring. J. Ambient Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-020-02720-6"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ebrahim, A.F., and Mohammed, O.A. (2018). Pre-processing of energy demand disaggregation based data mining techniques for household load demand forecasting. Inventions, 3.","DOI":"10.3390\/inventions3030045"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of deep learning algorithms and architectures","volume":"7","author":"Shrestha","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107826","DOI":"10.1016\/j.apacoust.2020.107826","article-title":"Supervised binaural source separation using auditory attention detection in realistic scenarios","volume":"175","author":"Zakeri","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xie, W., Wang, J., Xing, C., Guo, S., Guo, M., and Zhu, L. (2020). Variational Autoencoder Bidirectional Long and Short-term Memory Neural Network Soft-sensor Model Based on Batch Training Strategy. IEEE Trans. Ind. Inform.","DOI":"10.1109\/TII.2020.3025204"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., and Anvari-Moghaddam, A. (2020). Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Appl. Sci., 10.","DOI":"10.3390\/app10113829"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mansour-Saatloo, A., Moradzadeh, A., Mohammadi-Ivatloo, B., Ahmadian, A., and Elkamel, A. (2020). Machine learning based PEVs load extraction and analysis. Electronics, 9.","DOI":"10.3390\/electronics9071150"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"115873","DOI":"10.1016\/j.energy.2019.115873","article-title":"Multi-agent microgrid energy management based on deep learning forecaster","volume":"186","author":"Afrasiabi","year":"2019","journal-title":"Energy"}],"container-title":["Inventions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2411-5134\/6\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:19:19Z","timestamp":1760159959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2411-5134\/6\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,3]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["inventions6010015"],"URL":"https:\/\/doi.org\/10.3390\/inventions6010015","relation":{},"ISSN":["2411-5134"],"issn-type":[{"value":"2411-5134","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,3]]}}}