{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:44:20Z","timestamp":1760143460978,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T00:00:00Z","timestamp":1707436800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Opening Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems of Ministry of Education of China (Chongqing University)","award":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"],"award-info":[{"award-number":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"],"award-info":[{"award-number":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Opening Fund of State Key Laboratory of Green Building in Western China","award":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"],"award-info":[{"award-number":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"]}]},{"name":"open Foundation of Anhui Province Key Laboratory of Intelligent Building and Building Energy-saving","award":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"],"award-info":[{"award-number":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"]}]},{"name":"\u201cThe 14th Five Year Plan\u201d Hubei Provincial advantaged characteristic disciplines (groups) project of Wuhan University of Science and Technology","award":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"],"award-info":[{"award-number":["LLEUTS-202305","51906181","LSKF202316","IBES2022KF11","2023D0504"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Weather data errors affect energy management by influencing the accuracy of building energy predictions. This study presents a long short-term memory (LSTM) prediction model based on the \u201cEnergy Detective\u201d dataset (Shanghai, China) and neighboring weather station data. The study analyzes the errors of different weather data sources (Detective and A) at the same latitude and longitude. Subsequently, it discusses the effects of weather errors from neighboring weather stations (Detective, A, B, C, and D) on energy forecasts for the next hour and day including the selection process for neighboring weather stations. Furthermore, it compares the forecast results for summer and autumn. The findings indicate a correlation between weather errors from neighboring weather stations and energy consumption. The median R-Square for predicting the next hour reached 0.95. The model\u2019s predictions for the next day exhibit a higher Prediction Interval Mean Width (139.0 in summer and 146.1 in autumn), indicating a greater uncertainty.<\/jats:p>","DOI":"10.3390\/s24041157","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T11:42:05Z","timestamp":1707478925000},"page":"1157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Study on the Impact of Building Energy Predictions Considering Weather Errors of Neighboring Weather Stations"],"prefix":"10.3390","volume":"24","author":[{"given":"Guannan","family":"Li","sequence":"first","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"},{"name":"Anhui Province Key Laboratory of Intelligent Building and Building Energy-Saving, Anhui Jianzhu University, Hefei 230601, China"},{"name":"Key Laboratory of Low-Grade Energy Utilization Technologies and Systems (Chongqing University), Ministry of Education of China, Chongqing University, Chongqing 400044, China"},{"name":"State Key Laboratory of Green Building in Western China, Xi\u2019an University of Architecture & Technology, Xi\u2019an 710055, China"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]},{"given":"Chunzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]},{"given":"Chengliang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]},{"given":"Lei","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brusaferri, A., Matteucci, M., Spinelli, S., and Vitali, A. (2022). Probabilistic electric load forecasting through Bayesian Mixture Density Networks. Appl. Energy, 309.","DOI":"10.1016\/j.apenergy.2021.118341"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hong, T., and Piette, M.A. (2020). Building thermal load prediction through shallow machine learning and deep learning. Appl. Energy, 263.","DOI":"10.1016\/j.apenergy.2020.114683"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shamsi, M.H., Ali, U., Mangina, E., and O\u2019Donnell, J. (2020). A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models. Appl. Energy, 275.","DOI":"10.1016\/j.apenergy.2020.115141"},{"key":"ref_4","unstructured":"Hayes, K.R. (2011). Uncertainty and Uncertainty Analysis Methods Issues in Quantitative and Qualitative Risk Modeling with Application to Import Risk Assessment, Commonwealth Scientific and Industrial Research Organisation."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5735","DOI":"10.5194\/amt-14-5735-2021","article-title":"Interpreting estimated observation error statistics of weather radar measurements using the ICON-LAM-KENDA system","volume":"14","author":"Zeng","year":"2021","journal-title":"Atmos. Meas. Tech."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, S., Wang, S., and Shan, K. (2022). Flow measurement uncertainty quantification for building central cooling systems with multiple water-cooled chillers using a Bayesian approach. Appl. Therm. Eng., 202.","DOI":"10.1016\/j.applthermaleng.2021.117857"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.biosystemseng.2021.12.014","article-title":"Weather forecast error modelling and performance analysis of automatic greenhouse climate control","volume":"214","author":"Kuijpers","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nielsen, J.M., van de Beek, C.Z.R., Thorndahl, S., Olsson, J., Andersen, C.B., Andersson, J.C.M., Rasmussen, M.R., and Nielsen, J.E. (2024). Merging weather radar data and opportunistic rainfall sensor data to enhance rainfall estimates. Atmos. Res., 300.","DOI":"10.1016\/j.atmosres.2024.107228"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1007\/s10661-012-2831-6","article-title":"Detecting and correcting sensor drifts in long-term weather data","volume":"185","author":"Arx","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Impedovo, D., Abbattista, G., Convertini, N., Gattulli, V., Pirlo, G., and Sarcinella, L. (2021). Effective Machine Learning Solutions for Punctual Weather Parameter Forecasting in a Real Missing Data Scenario. Int. J. Pattern Recognit. Artif. Intell., 35.","DOI":"10.1142\/S0218001421600041"},{"key":"ref_11","first-page":"116","article-title":"A Statistical Analysis of Noisy Crowdsourced Weather Data","volume":"14","author":"Chakraborty","year":"2019","journal-title":"Statistics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.buildenv.2015.03.025","article-title":"Development of hybrid numerical and statistical short term horizon weather prediction models for building energy management optimisation","volume":"90","author":"Lazos","year":"2015","journal-title":"Build. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1080\/23744731.2018.1556052","article-title":"Experimental application of classification learning to generate simplified model predictive controls for a shared office heating system","volume":"25","author":"Bursill","year":"2019","journal-title":"Sci. Technol. Built Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s12273-013-0142-7","article-title":"A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting","volume":"7","author":"Dong","year":"2013","journal-title":"Build. Simul."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.apenergy.2014.12.019","article-title":"Modeling and forecasting energy consumption for heterogeneous buildings using a physical\u2013statistical approach","volume":"144","author":"Lu","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1007\/s12273-021-0877-5","article-title":"A statistical-based online cross-system fault detection method for building chillers","volume":"15","author":"Liu","year":"2022","journal-title":"Build. Simul."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, G., Li, F., Xu, C., and Fang, X. (2022). A spatial-temporal layer-wise relevance propagation method for improving interpretability and prediction accuracy of LSTM building energy prediction. Energy Build., 271.","DOI":"10.1016\/j.enbuild.2022.112317"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s12273-022-0935-7","article-title":"Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems","volume":"16","author":"Li","year":"2022","journal-title":"Build. Simul."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yoon, S. (2020). In-situ sensor calibration in an operational air-handling unit coupling autoencoder and Bayesian inference. Energy Build., 221.","DOI":"10.1016\/j.enbuild.2020.110026"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.apenergy.2019.02.052","article-title":"Deep learning-based feature engineering methods for improved building energy prediction","volume":"240","author":"Fan","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.egyr.2021.11.256","article-title":"Energetics Systems and artificial intelligence: Applications of industry 4.0","volume":"8","author":"Ahmad","year":"2022","journal-title":"Energy Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Choi, J.H., Lee, H.J., Oh, S., and Nam, K. (2022). Development of vehicle maneuvering system for autonomous driving. Mechatronics, 85.","DOI":"10.1016\/j.mechatronics.2022.102798"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isatra.2022.04.027","article-title":"Compound FAT-based prespecified performance learning control of robotic manipulators with actuator dynamics","volume":"131","author":"Keighobadi","year":"2022","journal-title":"ISA Trans."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shapi, M.K.M., Ramli, N.A., and Awalin, L.J. (2021). Energy consumption prediction by using machine learning for smart building: Case study in Malaysia. Dev. Built Environ., 5.","DOI":"10.1016\/j.dibe.2020.100037"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, S.-Y., Le, T.H.M., and Kim, Y.-M. (2023). Prediction and detection of potholes in urban roads: Machine learning and deep learning based image segmentation approaches. Dev. Built Environ., 13.","DOI":"10.1016\/j.dibe.2022.100109"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.enbuild.2016.11.009","article-title":"A relevant data selection method for energy consumption prediction of low energy building based on support vector machine","volume":"138","author":"Paudel","year":"2017","journal-title":"Energy Build."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s12273-020-0721-3","article-title":"Data-driven sensitivity analysis and electricity consumption prediction for water source heat pump system using limited information","volume":"14","author":"Sun","year":"2020","journal-title":"Build. Simul."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enbuild.2018.04.008","article-title":"Random Forest based hourly building energy prediction","volume":"171","author":"Wang","year":"2018","journal-title":"Energy Build."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1016\/j.apenergy.2008.11.035","article-title":"Applying support vector machine to predict hourly cooling load in the building","volume":"86","author":"Li","year":"2009","journal-title":"Appl. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.apenergy.2018.03.125","article-title":"Machine learning-based thermal response time ahead energy demand prediction for building heating systems","volume":"221","author":"Guo","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.apenergy.2017.03.064","article-title":"A short-term building cooling load prediction method using deep learning algorithms","volume":"195","author":"Fan","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.energy.2019.05.138","article-title":"Predicting plug loads with occupant count data through a deep learning approach","volume":"181","author":"Wang","year":"2019","journal-title":"Energy"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Liu, Y., Wang, D., and Liu, X. (2021). Comparison of machine-learning models for predicting short-term building heating load using operational parameters. Energy Build., 253.","DOI":"10.1016\/j.enbuild.2021.111505"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, C., Fang, Z., Xu, X., Zhang, X., Ding, Y., Jiang, X., and Ji, Y. (2020). Using long short-term memory networks to predict energy consumption of air-conditioning systems. Sustain. Cities Soc., 55.","DOI":"10.1016\/j.scs.2019.102000"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Torkzadeh, R., Mirzaei, A., Mirjalili, M.M., Anaraki, A.S., Sehhati, M.R., and Behdad, F. (2014, January 6\u20137). Medium term load forecasting in distribution systems based on multi linear regression & principal component analysis: A novel approach. Proceedings of the 19th Conference on Electrical Power Distribution Networks (EPDC), Tehran, Iran.","DOI":"10.1109\/EPDC.2014.6867500"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.ijepes.2010.01.009","article-title":"Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks","volume":"32","author":"Xia","year":"2010","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, G., Gao, J., Fang, X., Wang, C., and Xiong, C. (2023). Case Study: Impacts of Air-Conditioner Air Supply Strategy on Thermal Environment and Energy Consumption in Offices Using BES-CFD Co-Simulation. Sensors, 23.","DOI":"10.3390\/s23135958"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1109\/TPWRS.1987.4335210","article-title":"The Time Series Approach to Short Term Load Forecasting","volume":"2","author":"Martin","year":"1987","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xiao, T., Xu, P., He, R., and Sha, H. (2022). Status quo and opportunities for building energy prediction in limited data Context\u2014Overview from a competition. Appl. Energy, 305.","DOI":"10.1016\/j.apenergy.2021.117829"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.ijrefrig.2020.02.035","article-title":"Abnormal energy consumption detection for GSHP system based on ensemble deep learning and statistical modeling method","volume":"114","author":"Xu","year":"2020","journal-title":"Int. J. Refrig."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.neucom.2020.04.110","article-title":"Interpretable spatio-temporal attention LSTM model for flood forecasting","volume":"403","author":"Ding","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, G., Wang, K., Hao, X., Zhang, Z., Zhao, Y., and Xu, Q. (2022). SA-LSTMs: A new advance prediction method of energy consumption in cement raw materials grinding system. Energy, 241.","DOI":"10.1016\/j.energy.2021.122768"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s12273-021-0885-0","article-title":"Development of a key-variable-based parallel HVAC energy predictive model","volume":"15","author":"Sha","year":"2022","journal-title":"Build. Simul."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2022.09.009","article-title":"Quantifying the role of weather forecast error on the uncertainty of greenhouse energy prediction and power market trading","volume":"224","author":"Payne","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Wang, L. (2023). Optimization of site investigation program for reliability assessment of undrained slope using Spearman rank correlation coefficient. Comput. Geotech., 155.","DOI":"10.1016\/j.compgeo.2022.105208"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1157\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:58:03Z","timestamp":1760104683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1157"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,9]]},"references-count":45,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041157"],"URL":"https:\/\/doi.org\/10.3390\/s24041157","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,2,9]]}}}