{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T07:21:24Z","timestamp":1768288884691,"version":"3.49.0"},"reference-count":24,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.063809","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T02:58:11Z","timestamp":1744772291000},"page":"2907-2926","source":"Crossref","is-referenced-by-count":2,"title":["Bidirectional LSTM-Based Energy Consumption Forecasting: Advancing AI-Driven Cloud Integration for Cognitive City Energy Management"],"prefix":"10.32604","volume":"83","author":[{"given":"Sheik Mohideen","family":"Shah","sequence":"first","affiliation":[]},{"given":"Meganathan","family":"Selvamani","sequence":"additional","affiliation":[]},{"given":"Mahesh Thyluru","family":"Ramakrishna","sequence":"additional","affiliation":[]},{"given":"Surbhi Bhatia","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Shakila","family":"Basheer","sequence":"additional","affiliation":[]},{"given":"Wajdan Al","family":"Malwi","sequence":"additional","affiliation":[]},{"given":"Mohammad Tabrez","family":"Quasim","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"128575","DOI":"10.1016\/j.energy.2023.128575","article-title":"Gross electricity consumption forecasting using LSTM and SARIMA approaches: a case study of T\u00fcrkiye","volume":"284","author":"Bilgili","year":"2023","journal-title":"Energy"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1049\/stg2.12095","article-title":"Evolution of smart grids towards the Internet of energy: concept and essential components for deep decarbonisation","volume":"6","author":"Ghiasi","year":"2023","journal-title":"IET Smart Grid"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.ijforecast.2021.11.001","article-title":"Forecasting: theory and practice","volume":"38","author":"Petropoulos","year":"2022","journal-title":"Int J Forecast"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"539","DOI":"10.32604\/iasc.2022.019658","article-title":"Energy demand forecasting using fused machine learning approaches","volume":"31","author":"Ghazal","year":"2022","journal-title":"Intell Autom Soft Comput"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"8919","DOI":"10.3390\/en15238919","article-title":"Methods of forecasting electric energy consumption: a literature review","volume":"15","author":"Klyuev","year":"2022","journal-title":"Energies"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"100220","DOI":"10.1016\/j.jpse.2024.100220","article-title":"Natural gas consumption forecasting using a novel two-stage model based on improved sparrow search algorithm","volume":"5","author":"Qiao","year":"2025","journal-title":"J Pipeline Sci Eng"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"109792","DOI":"10.1016\/j.epsr.2023.109792","article-title":"State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques","volume":"225","author":"Wazirali","year":"2023","journal-title":"Elect Power Syst Res"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"31191","DOI":"10.1109\/ACCESS.2021.3060290","article-title":"On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach","volume":"9","author":"Farsi","year":"2021","journal-title":"IEEE Access"},{"key":"ref9","first-page":"01445987241269496","article-title":"Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction","author":"Alizadegan","year":"2024","journal-title":"Energy Explor Exploit"},{"key":"ref10","first-page":"100772","article-title":"Artificial intelligence-based forecasting models for integrated energy system management planning: an exploration of the prospects for South Africa","volume":"24","author":"Krishnamurthy","year":"2024","journal-title":"Energy Convers Manag: X"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.3390\/su12031109","article-title":"Power demand forecasting using long short-term memory (LSTM) deep-learning model for monitoring energy sustainability","volume":"12","author":"Choi","year":"2020","journal-title":"Sustainability"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3390\/fi15020046","article-title":"Prediction of energy production level in large PV plants through AUTO-encoder based neural-network (AUTO-NN) with restricted boltzmann feature extraction","volume":"15","author":"Ramesh","year":"2023","journal-title":"Fut Internet"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"101017","DOI":"10.1016\/j.esr.2022.101017","article-title":"Artificial intelligence and machine learning in energy systems: a bibliographic perspective","volume":"45","author":"Entezari","year":"2023","journal-title":"Energy Strategy Rev"},{"key":"ref14","first-page":"101967","article-title":"Deep machine learning-based power usage effectiveness prediction for sustainable cloud infrastructures","volume":"52","author":"Ounifi","year":"2022","journal-title":"Sustain Energy Technol Assess"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"107899","DOI":"10.1016\/j.epsr.2022.107899","article-title":"Method for evaluation on energy consumption of cloud computing data center based on deep reinforcement learning","volume":"208","author":"Ma","year":"2022","journal-title":"Elect Power Syst Res"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1007\/s10462-022-10286-2","article-title":"AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives","volume":"56","author":"Himeur","year":"2023","journal-title":"Artif Intell Rev"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.3390\/smartcities5040071","article-title":"Energy and sustainable development in smart cities: an overview","volume":"5","author":"Almihat","year":"2022","journal-title":"Smart Cities"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1795","DOI":"10.35833\/MPCE.2022.000683","article-title":"Situation awareness and sensitivity analysis for absorption of grid-connected renewable energy power generation integrating robust optimization and radial basis function neural network","volume":"11","author":"Zhang","year":"2023","journal-title":"J Mod Power Syst Clean Energy"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TPWRS.2022.3159493","article-title":"An image inpainting approach to short-term load forecasting","volume":"38","author":"Liu","year":"2022","journal-title":"IEEE Trans Power Syst"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1109\/TMM.2020.3042077","article-title":"Temporal action localization using long short-term dependency","volume":"23","author":"Zhou","year":"2020","journal-title":"IEEE Trans Multimedia"},{"key":"ref21","first-page":"176211","article-title":"Short-term load forecasting based on mutual information and BI-LSTM considering fluctuation in importance values of features","volume":"11","author":"Hu","year":"2023","journal-title":"IEEE Access"},{"key":"ref22","first-page":"2297","article-title":"Interpretable memristive LSTM network design for probabilistic residential load forecasting","volume":"69","author":"Li","year":"2022","journal-title":"IEEE Trans Circuits Syst I: Reg Papers"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"122012","DOI":"10.1016\/j.eswa.2023.122012","article-title":"A hybrid model for deep learning short-term power load forecasting based on feature extraction statistics techniques","volume":"238","author":"Fan","year":"2024","journal-title":"Expert Syst Appl"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/TPWRS.2023.3271325","article-title":"A novel sequence to sequence data modelling based CNN-LSTM algorithm for three years ahead monthly peak load forecasting","volume":"39","author":"Rubasinghe","year":"2024","journal-title":"IEEE Trans Power Syst"}],"container-title":["Computers, Materials & Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-2\/TSP_CMC_63809\/TSP_CMC_63809.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:08:35Z","timestamp":1763341715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n2\/60603"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.063809","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}