{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T17:27:44Z","timestamp":1778002064688,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["DSAIPA\/AI\/0099\/2019"],"award-info":[{"award-number":["DSAIPA\/AI\/0099\/2019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>A major challenge of today\u2019s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.<\/jats:p>","DOI":"10.3390\/electronics10101149","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T10:59:12Z","timestamp":1620817152000},"page":"1149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Forecasting Energy Consumption of Wastewater Treatment Plants with a Transfer Learning Approach for Sustainable Cities"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7143-5413","authenticated-orcid":false,"given":"Pedro","family":"Oliveira","sequence":"first","affiliation":[{"name":"ALGORITMI Centre, Department of Informatics, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1561-2897","authenticated-orcid":false,"given":"Bruno","family":"Fernandes","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, Department of Informatics, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-644X","authenticated-orcid":false,"given":"Cesar","family":"Analide","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, Department of Informatics, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3549-0754","authenticated-orcid":false,"given":"Paulo","family":"Novais","sequence":"additional","affiliation":[{"name":"ALGORITMI Centre, Department of Informatics, University of Minho, 4710-057 Braga, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"ref_1","unstructured":"(2021, January 21). World Urbanization Prospects-Population Division-United Nations. Available online: https:\/\/population.un.org\/wup\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2265","DOI":"10.1016\/j.rser.2007.05.001","article-title":"Energy, environment and sustainable development","volume":"12","author":"Omer","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Daw, J., Hallett, K., DeWolfe, J., and Venner, I. (2012). Energy Efficiency Strategies for Municipal Wastewater Treatment Facilities, National Renewable Energy Lab.(NREL).","DOI":"10.2172\/1036045"},{"key":"ref_4","unstructured":"Liu, F., Ouedraogo, A., Manghee, S., and Danilenko, A. (2012). A Primer on Energy Efficiency for Municipal Water and Wastewater Utilities, World Bank."},{"key":"ref_5","unstructured":"Frade, J., Lacasta, N., Mendes, P., Cardoso, P., Trindade, I., Newton, F., Franco, P., Serra, A., P\u00f3voa, C., and Narciso, F. (2021, January 22). PENSAAR 2020\u2013Uma Estrat\u00e9gia ao Servi\u00e7o da Popula\u00e7\u00e3o: Servi\u00e7os de Qualidade a um Pre\u00e7o Sustent\u00e1vel. Available online: https:\/\/www.apambiente.pt\/index.php?ref=16&subref=7&sub2ref=9&sub3ref=1098."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.rser.2017.04.109","article-title":"Electricity generation and GHG emission reduction potentials through different municipal solid waste management technologies: A comparative review","volume":"79","author":"Rajaeifar","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.resconrec.2016.12.005","article-title":"Efficiency assessment of urban wastewater treatment plants in China: Considering greenhouse gas emissions","volume":"120","author":"Zeng","year":"2017","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_8","unstructured":"De Haas, D., Foley, J., Marshall, B., Dancey, M., Vierboom, S., and Bartle-Smith, J. (2021, January 25). Benchmarking Wastewater Treatment Plant Energy Use in Australia. Available online: https:\/\/www.researchgate.net\/profile\/David-De-Haas-2\/publication\/276921977_Benchmarking_Wastewater_Treatment_Plant_Energy_Use_in_Australia\/links\/5599093e08ae793d137e2735\/Benchmarking-Wastewater-Treatment-Plant-Energy-Use-in-Australia.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, Z., Zou, Z., and Wang, L. (2019). Analysis and forecasting of the energy consumption in wastewater treatment plant. Math. Probl. Eng., 2019.","DOI":"10.1155\/2019\/8690898"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4908","DOI":"10.1109\/JSEN.2020.3030584","article-title":"A Data-Driven Soft Sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study","volume":"21","author":"Harrou","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"667","DOI":"10.2166\/wst.2012.563","article-title":"Energy consumption model for wastewater treatment process control","volume":"67","author":"Huang","year":"2013","journal-title":"Water Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103","DOI":"10.2166\/wrd.2016.196","article-title":"Analysis of energy efficiency and energy consumption costs: A case study for regional wastewater treatment plant in Malaysia","volume":"7","author":"Ramli","year":"2017","journal-title":"J. Water Reuse Desalin."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1016\/j.jclepro.2019.05.331","article-title":"Innovative information and communication technology (ICT) system for energy management of public utilities in a post-disaster region: Case study of a wastewater treatment plant in Fukushima","volume":"233","author":"Maki","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105420","DOI":"10.1016\/j.biombioe.2019.105420","article-title":"Optimization of the energy consumption in activated sludge process using deep learning selective modeling","volume":"132","author":"Oulebsir","year":"2020","journal-title":"Biomass Bioenergy"},{"key":"ref_15","first-page":"723","article-title":"Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step Approaches","volume":"31","author":"Fernandes","year":"2020","journal-title":"Informatica"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jin, X., Yang, N., Wang, X., Bai, Y., Su, T., and Kong, J. (2019). Integrated predictor based on decomposition mechanism for PM2.5 long-term prediction. Appl. Sci., 9.","DOI":"10.3390\/app9214533"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, T., Song, S., Li, S., Ma, L., Pan, S., and Han, L. (2019). Research on gas concentration prediction models based on LSTM multidimensional time series. Energies, 12.","DOI":"10.3390\/en12010161"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mbatha, N., and Bencherif, H. (2020). Time series analysis and forecasting using a novel hybrid LSTM data-driven model based on empirical wavelet transform applied to total column of ozone at Buenos aires, Argentina (1966\u20132017). Atmosphere, 11.","DOI":"10.3390\/atmos11050457"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chatterjee, A., Gerdes, M.W., and Martinez, S.G. (2020). Statistical explorations and univariate timeseries analysis on covid-19 datasets to understand the trend of disease spreading and death. Sensors, 20.","DOI":"10.3390\/s20113089"},{"key":"ref_20","first-page":"1688","article-title":"Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning","volume":"15","author":"Zhang","year":"2019","journal-title":"Transp. Transp. Sci."},{"key":"ref_21","unstructured":"Dong, X., Qian, L., and Huang, L. (2017, January 13\u201316). Short-term load forecasting in smart grid: A combined CNN and K-means clustering approach. Proceedings of the International Conference on Big Data and Smart Computing (BigComp), Jeju, Korea."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1007\/s12145-020-00477-2","article-title":"A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin","volume":"13","author":"Hussain","year":"2020","journal-title":"Earth Sci. Informatics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Oliveira, P., Fernandes, B., Aguiar, F., Pereira, M.A., Analide, C., and Novais, P. (2020, January 4\u20136). A Deep Learning Approach to Forecast the Influent Flow in Wastewater Treatment Plants. Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning, Guimar\u00e3es, Portugal.","DOI":"10.1007\/978-3-030-62362-3_32"},{"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":"Medsker, L., and Jain, L. (1999). Recurrent Neural Networks: Design and Applications, CRC Press.","DOI":"10.1201\/9781420049176"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Kang, D., Lv, Y., and Chen, Y. (2017, January 16\u201319). Short-term traffic flow prediction with LSTM recurrent neural network. Proceedings of the 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317872"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, B., Sun, S., Li, J., Lin, X., and Tian, Y. (2019). Traffic flow prediction using LSTM with feature enhancement. Neurocomputing, 320\u2013327.","DOI":"10.1016\/j.neucom.2018.12.016"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fente, D.N., and Singh, D.K. (2018, January 20\u201321). Weather forecasting using artificial neural network. Proceedings of the 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India.","DOI":"10.1109\/ICICCT.2018.8473167"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.eswa.2018.04.004","article-title":"Web traffic anomaly detection using C-LSTM neural networks","volume":"106","author":"Kim","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Feng, C., Li, T., and Chana, D. (2017, January 26\u201329). Multi-level anomaly detection in industrial control systems via package signatures and LSTM networks. Proceedings of the 47th Annual IEEE\/IFIP International Conference on Dependable Systems and Networks (DSN), Denver, CO, USA.","DOI":"10.1109\/DSN.2017.34"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_33","unstructured":"Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"117081","DOI":"10.1016\/j.energy.2020.117081","article-title":"Wind power forecasting using attention-based gated recurrent unit network","volume":"196","author":"Niu","year":"2020","journal-title":"Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3814","DOI":"10.1109\/TNNLS.2019.2946414","article-title":"Deep learning method based on gated recurrent unit and variational mode decomposition for short-term wind power interval prediction","volume":"31","author":"Wang","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liao, W., and Chang, Y. (2018). Gated recurrent unit network-based short-term photovoltaic forecasting. Energies, 11.","DOI":"10.3390\/en11082163"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fukushima, K., and Miyake, S. (1982). Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition, Springer.","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","article-title":"Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex","volume":"160","author":"Hubel","year":"1962","journal-title":"J. Physiol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., and Chen, M. (2014, January 10\u201312). Medical image classification with convolutional neural network. Proceedings of the 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore.","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"ref_41","unstructured":"Chen, C., Liu, M., Tuzel, O., and Xiao, J. (2016, January 20\u201324). R-CNN for small object detection. Proceedings of the 13th Asian Conference on Computer Vision, Taipei, Taiwan."}],"container-title":["Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-9292\/10\/10\/1149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:59:38Z","timestamp":1760162378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-9292\/10\/10\/1149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,12]]},"references-count":41,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["electronics10101149"],"URL":"https:\/\/doi.org\/10.3390\/electronics10101149","relation":{},"ISSN":["2079-9292"],"issn-type":[{"value":"2079-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,12]]}}}