{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T22:48:56Z","timestamp":1768085336854,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T00:00:00Z","timestamp":1606694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015974","name":"State Grid Henan Electric Power Company","doi-asserted-by":"publisher","award":["5217L0190006"],"award-info":[{"award-number":["5217L0190006"]}],"id":[{"id":"10.13039\/501100015974","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by employing deep multitask learning whose structure is constructed by deep belief network (DBN) and multitask regression layer. The DBN can extract abstract and effective characteristics in an unsupervised fashion, and the multitask regression layer above the DBN is used for supervised prediction. Then, subject to condition of practical demand and model integrity, the whole energy forecasting model is introduced, including preprocessing, normalization, input properties, training stage, and evaluating indicator. Finally, the validity of the algorithm and the accuracy of the energy forecasts for an industrial-park IES system are verified through the simulations using actual operating data from load system. The positive results turn out that the deep multitask learning has great prospects for load forecast.<\/jats:p>","DOI":"10.3390\/e22121355","type":"journal-article","created":{"date-parts":[[2020,11,30]],"date-time":"2020-11-30T10:26:12Z","timestamp":1606731972000},"page":"1355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System"],"prefix":"10.3390","volume":"22","author":[{"given":"Linjuan","family":"Zhang","sequence":"first","affiliation":[{"name":"State Grid Henan Economic Research Institute, Zhengzhou 450052, China"}]},{"given":"Jiaqi","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China"}]},{"given":"Lili","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Henan Economic Research Institute, Zhengzhou 450052, China"}]},{"given":"Changqing","family":"Xu","sequence":"additional","affiliation":[{"name":"State Grid Henan Economic Research Institute, Zhengzhou 450052, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,30]]},"reference":[{"key":"ref_1","unstructured":"Zhang, T.W., and Sun, Y.L. (2012). The Third Industrial Revolution, CITIC Press."},{"key":"ref_2","first-page":"3482","article-title":"Technical Forms and Key Technologies on Integrated energy system","volume":"35","author":"Shiming","year":"2015","journal-title":"Proc. CSEE"},{"key":"ref_3","first-page":"4571","article-title":"The Optimization Control and Implementation for the Special Integrated energy system","volume":"35","author":"Qiuye","year":"2015","journal-title":"Proc. CSEE"},{"key":"ref_4","unstructured":"Hong, T. (2013). Energy forecasting: Past, present and future. Foresight, 43\u201348. Available online: http:\/\/prac.im.pwr.edu.pl\/~hugo\/RePEc\/wuu\/wpaper\/HSC_13_15.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.ijforecast.2015.11.011","article-title":"Probabilistic electric load forecasting: A tutorial review","volume":"32","author":"Hong","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tian, C., and Hao, Y. (2018). A novel nonlinear combined forecasting system for short-term load forecasting. Energies, 11.","DOI":"10.3390\/en11040712"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5724","DOI":"10.1109\/TSG.2018.2890809","article-title":"Machine Learning-Based Anomaly Detection for Load Forecasting under Cyberattacks","volume":"10","author":"Cui","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5132","DOI":"10.1109\/TIE.2019.2928275","article-title":"Multi-task Bayesian spatiotemporal Gaussian processes for short-term load forecasting","volume":"67","author":"Gilanifar","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Javaid, N., Mateen, A., Awais, M., and Khan, Z. (2019). Short-term load forecasting in smart grids: An intelligent modular approach. Energies, 12.","DOI":"10.3390\/en12010164"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Brown, R.H., Vitullo, S.R., Corliss, G.F., Adya, M., Kaefer, P.E., and Povinelli, R.J. (2015, January 26\u201330). Detrending daily natural gas consumption series to improve short-term forecasts. Proceedings of the 2015 IEEE Power & Energy Society General Meeting, Denver, CO, USA.","DOI":"10.1109\/PESGM.2015.7286138"},{"key":"ref_11","unstructured":"Kawashima, M., Dorgan, C.E., and Mitchell, J.W. (1995). Hourly Thermal Load Prediction for the Next 24 Hours by ARIMA, EWMA, LR and an Artificial Neural Network, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TASL.2006.889790","article-title":"Efficient WFST-Based One-Pass Decoding With On-The-Fly Hypothesis Rescoring in Extremely Large Vocabulary Continuous Speech Recognition","volume":"Volume 15","author":"Hori","year":"2007","journal-title":"IEEE Transactions on Audio Speech & Language Processing"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gupta, V., Gavrilovska, A., Schwan, K., Kharche, H., Tolia, N., Talwar, V., and Ranganathan, P. (2009, January 31). GViM: GPU-accelerated virtual machines. Proceedings of the 3rd ACM Workshop on System-Level Virtualization for High Performance Computing, HPCVirt\u201909, Nuremburg, Germany.","DOI":"10.1145\/1519138.1519141"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.apenergy.2016.08.108","article-title":"Deep belief network based deterministic and probabilistic wind speed forecasting approach","volume":"182","author":"Wang","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/TSTE.2015.2434387","article-title":"Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting","volume":"6","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ryu, S., Noh, J., and Kim, H. (2016). Deep neural network based demand side short term load forecasting. Energies, 10.","DOI":"10.3390\/en10010003"},{"key":"ref_18","first-page":"625","article-title":"Why Does Unsupervised Pre-training Help Deep Learning?","volume":"11","author":"Erhan","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","unstructured":"Haykin, S. (2007). Neural Networks: A Comprehensive Foundation, Prentice-Hall, Inc.. [3rd ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"1960","journal-title":"Neural Comput."},{"key":"ref_21","unstructured":"Carreira-Perpinan, M.A., and Hinton, G.E. (2005, January 6\u20138). On contrastive divergence learning. Proceedings of the Artificial Intelligence & Statistics, Bridgetown, Barbados."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2009). Learning Deep Architectures for AI, Now Publishers Inc.. Foundations and Trends in Machine Learning.","DOI":"10.1561\/9781601982957"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask Learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1049\/iet-rpg.2014.0419","article-title":"Recognition and assessment of different factors which affect flicker in wind turbines","volume":"10","author":"Fooladi","year":"2016","journal-title":"Renew. Power Gener. IET"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/12\/1355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:39:47Z","timestamp":1760179187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/12\/1355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,30]]},"references-count":24,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["e22121355"],"URL":"https:\/\/doi.org\/10.3390\/e22121355","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,30]]}}}