{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T22:45:11Z","timestamp":1775947511715,"version":"3.50.1"},"reference-count":71,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114517","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:28:22Z","timestamp":1774067302000},"page":"114517","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A transformer-based deep neural network long-term forecasting of solar irradiance and temperature: A multivariate multistep multitarget approach"],"prefix":"10.1016","volume":"174","author":[{"given":"Iman","family":"Baghaei","sequence":"first","affiliation":[]},{"given":"Amirmohammad","family":"Shirazizadeh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4747-505X","authenticated-orcid":false,"given":"Rouhollah","family":"Ahmadi","sequence":"additional","affiliation":[]},{"given":"Alireza","family":"Zahedi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1357-0470","authenticated-orcid":false,"given":"Mojtaba","family":"Mirhosseini","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"9","key":"10.1016\/j.engappai.2026.114517_bib1","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1002\/ese3.1226","article-title":"\"Transformer-based time series prediction of the maximum power point for solar photovoltaic cells,\"","volume":"10","author":"Agrawal","year":"2022","journal-title":"Energy Sci. Eng."},{"key":"10.1016\/j.engappai.2026.114517_bib2","doi-asserted-by":"crossref","first-page":"109792","DOI":"10.1016\/j.rser.2020.109792","article-title":"\"A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization,\"","volume":"124","author":"Ahmed","year":"2020","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"2","key":"10.1016\/j.engappai.2026.114517_bib3","doi-asserted-by":"crossref","first-page":"901","DOI":"10.3390\/s23020901","article-title":"\"CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: a Case Study of the High-Voltage Subnet of Northeast Germany,\"","volume":"23","author":"Aksan","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114517_bib4","first-page":"I","article-title":"\"Attention is all you need,\"","volume":"30","author":"Ashish","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114517_bib5","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.renene.2023.01.102","article-title":"\"Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output,\"","volume":"206","author":"Azizi","year":"2023","journal-title":"Renew. Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib6","article-title":"\"Longformer: the long-document transformer,\"","author":"Beltagy","year":"2020","journal-title":"arXiv preprint arXiv:2004.05150"},{"key":"10.1016\/j.engappai.2026.114517_bib7","article-title":"Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs in python","author":"Brownlee","year":"2018","journal-title":"Machine Learning Mastery"},{"key":"10.1016\/j.engappai.2026.114517_bib8","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"12270","article-title":"Autoformer: searching transformers for visual recognition","author":"Chen","year":"2021"},{"key":"10.1016\/j.engappai.2026.114517_bib9","doi-asserted-by":"crossref","first-page":"126034","DOI":"10.1016\/j.energy.2022.126034","article-title":"\"A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks,\"","volume":"263","author":"Chengqing","year":"2023","journal-title":"Energy"},{"issue":"no. 10","key":"10.1016\/j.engappai.2026.114517_bib10","article-title":"\"Intra-hour irradiance forecasting techniques for solar power integration: a review,\" iscience,","volume":"24","author":"Chu","year":"2021"},{"key":"10.1016\/j.engappai.2026.114517_bib11","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.rser.2017.08.017","article-title":"\"Forecasting of photovoltaic power generation and model optimization: a review,\"","volume":"81","author":"Das","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"5","key":"10.1016\/j.engappai.2026.114517_bib12","doi-asserted-by":"crossref","first-page":"100113","DOI":"10.1016\/j.geits.2023.100113","article-title":"\"Power output forecasting of solar photovoltaic plant using LSTM,\"","volume":"2","author":"Dhaked","year":"2023","journal-title":"Green Energy Intell. Transp."},{"key":"10.1016\/j.engappai.2026.114517_bib13","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/9620945","article-title":"\"Global solar radiation forecasting using square root regularization-based ensemble,\"","volume":"2019","author":"Dong","year":"2019","journal-title":"Math. Probl Eng."},{"issue":"4","key":"10.1016\/j.engappai.2026.114517_bib14","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1177\/17442591231218831","article-title":"\"Multi-step solar radiation prediction using transformer: a case study from solar radiation data in Tokyo,\"","volume":"47","author":"Dong","year":"2024","journal-title":"J. Build. Phys."},{"issue":"6","key":"10.1016\/j.engappai.2026.114517_bib15","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.3390\/en15062150","article-title":"\"Short-term solar power predicting model based on multi-step CNN stacked LSTM technique,\"","volume":"15","author":"Elizabeth Michael","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.engappai.2026.114517_bib16","doi-asserted-by":"crossref","first-page":"31692","DOI":"10.1109\/ACCESS.2022.3160484","article-title":"\"Solar power forecasting using deep learning techniques,\"","volume":"10","author":"Elsaraiti","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114517_bib17","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.psep.2020.10.048","article-title":"\"Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia,\"","volume":"149","author":"Elsheikh","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"10.1016\/j.engappai.2026.114517_bib18","doi-asserted-by":"crossref","first-page":"116475","DOI":"10.1016\/j.enconman.2022.116475","article-title":"\"Modeling and analysis of hybrid solar water desalination system for different scenarios in Indonesia,\"","volume":"276","author":"Fairuz","year":"2023","journal-title":"Energy Convers. Manag."},{"issue":"4","key":"10.1016\/j.engappai.2026.114517_bib19","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1002\/pip.1239","article-title":"\"Specific energy consumption of PV reverse osmosis systems. Experiment and theory,\"","volume":"21","author":"Fraidenraich","year":"2013","journal-title":"Prog. Photovoltaics Res. Appl."},{"key":"10.1016\/j.engappai.2026.114517_bib20","doi-asserted-by":"crossref","first-page":"100078","DOI":"10.1016\/j.clet.2021.100078","article-title":"\"Performance evaluation and economics of a locally-made stand-alone hybrid photovoltaic-thermal brackish water reverse osmosis unit,\"","volume":"2","author":"Gorjian","year":"2021","journal-title":"Cleaner Engineering and Technology"},{"key":"10.1016\/j.engappai.2026.114517_bib21","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.renene.2022.07.136","article-title":"\"Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad,\"","volume":"198","author":"Haider","year":"2022","journal-title":"Renew. Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib22","doi-asserted-by":"crossref","first-page":"1055683","DOI":"10.3389\/fenrg.2022.1055683","article-title":"\"Deep learning model-transformer based wind power forecasting approach,\"","volume":"10","author":"Huang","year":"2023","journal-title":"Front. Energy Res."},{"issue":"1","key":"10.1016\/j.engappai.2026.114517_bib23","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/TSMC.2021.3093519","article-title":"\"Automated deep CNN-LSTM architecture design for solar irradiance forecasting,\"","volume":"52","author":"Jalali","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern.: Systems"},{"key":"10.1016\/j.engappai.2026.114517_bib24","doi-asserted-by":"crossref","first-page":"122155","DOI":"10.1016\/j.apenergy.2023.122155","article-title":"\"Applicability analysis of transformer to wind speed forecasting by a novel deep learning framework with multiple atmospheric variables,\"","volume":"353","author":"Jiang","year":"2024","journal-title":"Appl. Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib25","doi-asserted-by":"crossref","first-page":"119476","DOI":"10.1016\/j.jclepro.2019.119476","article-title":"\"Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: case study of South Korea,\"","volume":"250","author":"Jung","year":"2020","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.engappai.2026.114517_bib26","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.desal.2017.04.006","article-title":"\"Analysis of specific energy consumption in reverse osmosis desalination processes,\"","volume":"431","author":"Karabelas","year":"2018","journal-title":"Desalination"},{"key":"10.1016\/j.engappai.2026.114517_bib27","first-page":"110063","article-title":"N. ul Islam, \"A deep learning-based transformer model for photovoltaic fault forecasting and classification,\" electric power systems research,","volume":"228","author":"Khalil","year":"2024"},{"key":"10.1016\/j.engappai.2026.114517_bib28","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4361","article-title":"\"Efficient two-stage model retraining for machine unlearning,\"","author":"Kim","year":"2022"},{"key":"10.1016\/j.engappai.2026.114517_bib29","article-title":"\"Reformer: the efficient transformer,\"","author":"Kitaev","year":"2020","journal-title":"arXiv preprint arXiv:2001.04451"},{"key":"10.1016\/j.engappai.2026.114517_bib30","doi-asserted-by":"crossref","first-page":"114213","DOI":"10.1016\/j.desal.2019.114213","article-title":"\"Analysis of temperature effects on the specific energy consumption in reverse osmosis desalination processes,\"","volume":"476","author":"Koutsou","year":"2020","journal-title":"Desalination"},{"key":"10.1016\/j.engappai.2026.114517_bib31","doi-asserted-by":"crossref","first-page":"128566","DOI":"10.1016\/j.jclepro.2021.128566","article-title":"\"Deep learning models for solar irradiance forecasting: a comprehensive review,\"","volume":"318","author":"Kumari","year":"2021","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.engappai.2026.114517_bib32","article-title":"\"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,\" advances in neural information processing systems,","volume":"32","author":"Li","year":"2019"},{"issue":"6","key":"10.1016\/j.engappai.2026.114517_bib33","doi-asserted-by":"crossref","first-page":"993","DOI":"10.3390\/w14060993","article-title":"\"Prediction of flow based on a CNN-LSTM combined deep learning approach,\"","volume":"14","author":"Li","year":"2022","journal-title":"Water"},{"key":"10.1016\/j.engappai.2026.114517_bib34","first-page":"1748","article-title":"\"Temporal fusion transformers for interpretable multi-horizon time series forecasting,\" international journal of forecasting, vol","volume":"4","author":"Lim","year":"2021","journal-title":"37, no"},{"key":"10.1016\/j.engappai.2026.114517_bib35","doi-asserted-by":"crossref","first-page":"126100","DOI":"10.1016\/j.energy.2022.126100","article-title":"\"Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model,\"","volume":"263","author":"Lv","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib36","article-title":"\"Cost-effective retraining of machine learning models,\"","author":"Mahadevan","year":"2023","journal-title":"arXiv preprint arXiv:2310.04216"},{"key":"10.1016\/j.engappai.2026.114517_bib37","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.energy.2014.02.024","article-title":"\"Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran,\"","volume":"67","author":"Mohammadi","year":"2014","journal-title":"Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib39","doi-asserted-by":"crossref","first-page":"127678","DOI":"10.1016\/j.energy.2023.127678","article-title":"\"A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy,\"","volume":"278","author":"Nascimento","year":"2023","journal-title":"Energy"},{"issue":"9","key":"10.1016\/j.engappai.2026.114517_bib40","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.3390\/w13091282","article-title":"\"Unraveling the water-energy-food-environment nexus for climate change adaptation in Iran: urmia Lake Basin case-study,\"","volume":"13","author":"Nasrollahi","year":"2021","journal-title":"Water"},{"key":"10.1016\/j.engappai.2026.114517_bib41","doi-asserted-by":"crossref","first-page":"116639","DOI":"10.1016\/j.enconman.2022.116639","article-title":"\"Techno-economic optimization of a renewable micro grid using multi-objective particle swarm optimization algorithm,\"","volume":"277","author":"Parvin","year":"2023","journal-title":"Energy Convers. Manag."},{"key":"10.1016\/j.engappai.2026.114517_bib42","doi-asserted-by":"crossref","first-page":"100314","DOI":"10.1016\/j.egyai.2023.100314","article-title":"\"Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction data,\"","volume":"15","author":"Pereira","year":"2024","journal-title":"Energy AI"},{"issue":"17","key":"10.1016\/j.engappai.2026.114517_bib43","doi-asserted-by":"crossref","first-page":"8852","DOI":"10.3390\/app12178852","article-title":"\"Solar Irradiance Forecasting with Transformer Model,\"","volume":"12","author":"Posp\u00edchal","year":"2022","journal-title":"Appl. Sci."},{"issue":"3","key":"10.1016\/j.engappai.2026.114517_bib44","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.solener.2008.08.007","article-title":"\"Predicting solar radiation at high resolutions: a comparison of time series forecasts,\"","volume":"83","author":"Reikard","year":"2009","journal-title":"Sol. Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib45","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1016\/j.rser.2017.02.081","article-title":"\"Photovoltaic solar energy: conceptual framework,\"","volume":"74","author":"Sampaio","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"9","key":"10.1016\/j.engappai.2026.114517_bib46","doi-asserted-by":"crossref","first-page":"3245","DOI":"10.1073\/pnas.1222460110","article-title":"\"Multimodel assessment of water scarcity under climate change,\"","volume":"111","author":"Schewe","year":"2014","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.engappai.2026.114517_bib47","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s13762-018-1779-7","article-title":"\"Potential of solar energy in Iran for carbon dioxide mitigation,\"","volume":"16","author":"Shahsavari","year":"2019","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.114517_bib48","doi-asserted-by":"crossref","first-page":"1353","DOI":"10.3390\/en16031353","article-title":"\"Transformers-Based Encoder Model for Forecasting Hourly Power Output of Transparent Photovoltaic Module Systems,\"","volume":"16","author":"Sherozbek","year":"2023","journal-title":"Energies"},{"issue":"24","key":"10.1016\/j.engappai.2026.114517_bib49","doi-asserted-by":"crossref","first-page":"6601","DOI":"10.3390\/en13246601","article-title":"\"Prediction of sorption processes using the deep learning methods (long short-term memory),\"","volume":"13","author":"Skrobek","year":"2020","journal-title":"Energies"},{"key":"10.1016\/j.engappai.2026.114517_bib50","doi-asserted-by":"crossref","first-page":"103190","DOI":"10.1016\/j.advengsoft.2022.103190","article-title":"\"Implementation of deep learning methods in prediction of adsorption processes,\"","volume":"173","author":"Skrobek","year":"2022","journal-title":"Adv. Eng. Software"},{"key":"10.1016\/j.engappai.2026.114517_bib52","doi-asserted-by":"crossref","first-page":"112473","DOI":"10.1016\/j.rser.2022.112473","article-title":"\"Photovoltaic power forecasting: a hybrid deep learning model incorporating transfer learning strategy,\"","volume":"162","author":"Tang","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.engappai.2026.114517_bib53","doi-asserted-by":"crossref","first-page":"111758","DOI":"10.1016\/j.rser.2021.111758","article-title":"\"A review of very short-term wind and solar power forecasting,\"","volume":"153","author":"Tawn","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"2","key":"10.1016\/j.engappai.2026.114517_bib54","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1177\/0309524X19849867","article-title":"\"Short-term wind speed forecasting based on autoregressive moving average with echo state network compensation,\"","volume":"44","author":"Tian","year":"2020","journal-title":"Wind Eng."},{"key":"10.1016\/j.engappai.2026.114517_bib55","article-title":"\"Attention is all you need,\" advances in neural information processing systems,","volume":"30","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.engappai.2026.114517_bib56","doi-asserted-by":"crossref","first-page":"128146","DOI":"10.1016\/j.jclepro.2021.128146","article-title":"\"Multi-criteria decision analysis for optimal planning of desalination plant feasibility in different urban cities in India,\"","volume":"315","author":"Vishnupriyan","year":"2021","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.engappai.2026.114517_bib57","doi-asserted-by":"crossref","first-page":"113315","DOI":"10.1016\/j.apenergy.2019.113315","article-title":"\"A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network,\"","volume":"251","author":"Wang","year":"2019","journal-title":"Appl. Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib58","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1016\/j.solener.2021.12.012","article-title":"\"The cost of day-ahead solar forecasting errors in the United States,\"","volume":"231","author":"Wang","year":"2022","journal-title":"Sol. Energy"},{"key":"10.1016\/j.engappai.2026.114517_bib59","doi-asserted-by":"crossref","first-page":"125592","DOI":"10.1016\/j.energy.2022.125592","article-title":"\"Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model,\"","volume":"262","author":"Wang","year":"2023","journal-title":"Energy"},{"issue":"21","key":"10.1016\/j.engappai.2026.114517_bib60","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.3390\/en12214055","article-title":"\"Hour-ahead solar irradiance forecasting using multivariate gated recurrent units,\"","volume":"12","author":"Wojtkiewicz","year":"2019","journal-title":"Energies"},{"key":"10.1016\/j.engappai.2026.114517_bib61","first-page":"22419","article-title":"\"Autoformer: decomposition transformers with auto-correlation for long-term series forecasting,\" advances in neural information processing systems,","volume":"34","author":"Wu","year":"2021"},{"issue":"5","key":"10.1016\/j.engappai.2026.114517_bib62","doi-asserted-by":"crossref","first-page":"651","DOI":"10.3390\/atmos12050651","article-title":"\"A short-term wind speed forecasting model based on a multi-variable long short-term memory network,\"","volume":"12","author":"Xie","year":"2021","journal-title":"Atmosphere"},{"key":"10.1016\/j.engappai.2026.114517_bib63","doi-asserted-by":"crossref","first-page":"116760","DOI":"10.1016\/j.enconman.2023.116760","article-title":"\"Deep learning-based multistep ahead wind speed and power generation forecasting using direct method,\"","volume":"281","author":"Yaghoubirad","year":"2023","journal-title":"Energy Convers. Manag."},{"issue":"10","key":"10.1016\/j.engappai.2026.114517_bib64","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0285410","article-title":"\"Short-term solar energy forecasting: integrated computational intelligence of LSTMs and GRU,\"","volume":"18","author":"Zameer","year":"2023","journal-title":"PLoS One"},{"key":"10.1016\/j.engappai.2026.114517_bib65","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"\"Time series forecasting using a hybrid ARIMA and neural network model,\"","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.engappai.2026.114517_bib66","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","article-title":"\"Forecasting with artificial neural networks:: the state of the art,\"","volume":"14","author":"Zhang","year":"1998","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.engappai.2026.114517_bib67","series-title":"Data Mining Applications with R","author":"Zhao","year":"2013"},{"issue":"4","key":"10.1016\/j.engappai.2026.114517_bib68","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1016\/j.enconman.2010.11.007","article-title":"\"Fine tuning support vector machines for short-term wind speed forecasting,\"","volume":"52","author":"Zhou","year":"2011","journal-title":"Energy Convers. Manag."},{"key":"10.1016\/j.engappai.2026.114517_bib69","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.engappai.2026.114517_bib70","series-title":"International Conference on Machine Learning","first-page":"27268","article-title":"\"Fedformer: frequency enhanced decomposed transformer for long-term series forecasting,\"","author":"Zhou","year":"2022"},{"issue":"40","key":"10.1016\/j.engappai.2026.114517_bib71","doi-asserted-by":"crossref","first-page":"15317","DOI":"10.1016\/j.ijhydene.2023.01.068","article-title":"\"A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism,\"","volume":"48","author":"Zhou","year":"2023","journal-title":"Int. J. Hydrogen Energy"},{"issue":"13","key":"10.1016\/j.engappai.2026.114517_bib72","doi-asserted-by":"crossref","first-page":"6010","DOI":"10.1021\/ie800735q","article-title":"\"Effect of thermodynamic restriction on energy cost optimization of RO membrane water desalination,\"","volume":"48","author":"Zhu","year":"2009","journal-title":"Ind. Eng. Chem. Res."},{"issue":"21","key":"10.1016\/j.engappai.2026.114517_bib73","doi-asserted-by":"crossref","first-page":"9581","DOI":"10.1021\/ie900729x","article-title":"\"Energy consumption optimization of reverse osmosis membrane water desalination subject to feed salinity fluctuation,\"","volume":"48","author":"Zhu","year":"2009","journal-title":"Ind. Eng. Chem. Res."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007980?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007980?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T22:05:33Z","timestamp":1775945133000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626007980"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":71,"alternative-id":["S0952197626007980"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114517","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A transformer-based deep neural network long-term forecasting of solar irradiance and temperature: A multivariate multistep multitarget approach","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114517","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114517"}}