{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:24:59Z","timestamp":1766298299595,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","funder":[{"DOI":"10.13039\/100018700","name":"HORIZON EUROPE Climate, Energy and Mobility","doi-asserted-by":"publisher","award":["101138373"],"award-info":[{"award-number":["101138373"]}],"id":[{"id":"10.13039\/100018700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,6,17]]},"DOI":"10.1145\/3679240.3734635","type":"proceedings-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T13:13:42Z","timestamp":1750079622000},"page":"677-686","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Training Size Matters: Impact of Training Data Size on Electrical Load Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5004-5600","authenticated-orcid":false,"given":"Stepan","family":"Gagin","sequence":"first","affiliation":[{"name":"University of Passau, Passau, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8765-9331","authenticated-orcid":false,"given":"Alexander","family":"Tekles","sequence":"additional","affiliation":[{"name":"University of Passau, Passau, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3466-8135","authenticated-orcid":false,"given":"Hermann","family":"de Meer","sequence":"additional","affiliation":[{"name":"University of Passau, Passau, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Kadir Amasyali and Nora\u00a0M. El-Gohary. 2018. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews 81 (2018) 1192\u20131205. 10.1016\/j.rser.2017.04.095","DOI":"10.1016\/j.rser.2017.04.095"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Tasarruf Bashir Chen Haoyong Muhammad\u00a0Faizan Tahir and Zhu Liqiang. 2022. Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN. Energy Reports 8 (2022) 1678\u20131686. 10.1016\/j.egyr.2021.12.067","DOI":"10.1016\/j.egyr.2021.12.067"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","unstructured":"Mathieu Bourdeau Xiao\u00a0qiang Zhai Elyes Nefzaoui Xiaofeng Guo and Patrice Chatellier. 2019. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society 48 (2019) 101533. 10.1016\/j.scs.2019.101533","DOI":"10.1016\/j.scs.2019.101533"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","unstructured":"Young\u00a0Tae Chae Raya Horesh Youngdeok Hwang and Young\u00a0M. Lee. 2016. Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy and Buildings 111 (2016) 184\u2013194. 10.1016\/j.enbuild.2015.11.045","DOI":"10.1016\/j.enbuild.2015.11.045"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"Wonjun Choi and Sangwon Lee. 2023. Performance evaluation of deep learning architectures for load and temperature forecasting under dataset size constraints and seasonality. Energy and Buildings 288 (2023) 113027. 10.1016\/j.enbuild.2023.113027","DOI":"10.1016\/j.enbuild.2023.113027"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Chirag Deb Fan Zhang Junjing Yang Siew\u00a0Eang Lee and Kwok\u00a0Wei Shah. 2017. A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews 74 (2017) 902\u2013924. 10.1016\/j.rser.2017.02.085","DOI":"10.1016\/j.rser.2017.02.085"},{"key":"e_1_3_3_1_8_2","unstructured":"Azul Garza and Max Mergenthaler-Canseco. 2023. TimeGPT-1. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.03589 (2023)."},{"key":"e_1_3_3_1_9_2","unstructured":"GitHub facebook\/prophet. 2024. Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. https:\/\/github.com\/facebook\/prophet. [Accessed 21-11-2024]."},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Sarah Hadri Mehdi Najib Mohamed Bakhouya Youssef Fakhri and Mohamed El\u00a0Arroussi. 2021. Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings. Energies 14 18 (2021) 5831. 10.3390\/en14185831","DOI":"10.3390\/en14185831"},{"key":"e_1_3_3_1_11_2","unstructured":"Julien Herzen Francesco L\u00e4ssig Samuele\u00a0Giuliano Piazzetta Thomas Neuer L\u00e9o Tafti Guillaume Raille Tomas Van\u00a0Pottelbergh Marek Pasieka Andrzej Skrodzki Nicolas Huguenin et\u00a0al. 2022. Darts: User-Friendly Modern Machine Learning for Time Series. Journal of Machine Learning Research 23 124 (2022) 1\u20136. http:\/\/jmlr.org\/papers\/v23\/21-1177.html"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","unstructured":"Goopyo Hong and Namchul Seong. 2023. Optimization of the ANN Model for Energy Consumption Prediction of Direct-Fired Absorption Chillers for a Short-Term. Buildings 13 10 (2023) 2526. 10.3390\/buildings13102526","DOI":"10.3390\/buildings13102526"},{"key":"e_1_3_3_1_13_2","volume-title":"Forecasting: principles and practice (3rd ed.)","author":"Hyndman R.J.","year":"2021","unstructured":"R.J. Hyndman and G. Athanasopoulos. 2021. Forecasting: principles and practice (3rd ed.). OTexts, Melbourne, Australia. OTexts.com\/fpp3.Accessed on 01 May 2024."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Rob\u00a0J Hyndman and Anne\u00a0B Koehler. 2006. Another look at measures of forecast accuracy. International journal of forecasting 22 4 (2006) 679\u2013688.","DOI":"10.1016\/j.ijforecast.2006.03.001"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Jorjeta\u00a0G. Jetcheva Mostafa Majidpour and Wei-Peng Chen. 2014. Neural network model ensembles for building-level electricity load forecasts. Energy and Buildings 84 (2014) 214\u2013223. 10.1016\/j.enbuild.2014.08.004","DOI":"10.1016\/j.enbuild.2014.08.004"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Jee-Heon Kim Nam-Chul Seong and Wonchang Choi. 2020. Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models. Energies 13 17 (2020) 4361. 10.3390\/en13174361","DOI":"10.3390\/en13174361"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","unstructured":"M. Le\u00a0Cam A. Daoud and R. Zmeureanu. 2016. Forecasting electric demand of supply fan using data mining techniques. Energy 101 (2016) 541\u2013557. 10.1016\/j.energy.2016.02.061","DOI":"10.1016\/j.energy.2016.02.061"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","unstructured":"Guannan Li Xiaowei Zhao Cheng Fan Xi Fang Fan Li and Yubei Wu. 2021. Assessment of long short-term memory and its modifications for enhanced short-term building energy predictions. Journal of Building Engineering 43 (2021) 103182. 10.1016\/j.jobe.2021.103182","DOI":"10.1016\/j.jobe.2021.103182"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Yingkang Lu Buyun Sheng Gaocai Fu Ruiping Luo Geng Chen and Yuzhe Huang. 2023. Prophet-EEMD-LSTM based method for predicting energy consumption in the paint workshop. Applied Soft Computing 143 (2023) 110447. 10.1016\/j.asoc.2023.110447","DOI":"10.1016\/j.asoc.2023.110447"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","unstructured":"Spyros Makridakis Evangelos Spiliotis and Vassilios Assimakopoulos. 2018. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE 13 3 (03 2018) 1\u201326. 10.1371\/journal.pone.0194889","DOI":"10.1371\/journal.pone.0194889"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Mohammad\u00a0Azhar Mat\u00a0Daut Mohammad\u00a0Yusri Hassan Hayati Abdullah Hasimah\u00a0Abdul Rahman Md\u00a0Pauzi Abdullah and Faridah Hussin. 2017. Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review. Renewable and Sustainable Energy Reviews 70 (2017) 1108\u20131118. 10.1016\/j.rser.2016.12.015","DOI":"10.1016\/j.rser.2016.12.015"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Clayton Miller Anjukan Kathirgamanathan Bianca Picchetti Pandarasamy Arjunan June\u00a0Young Park Zoltan Nagy Paul Raftery Brodie\u00a0W Hobson Zixiao Shi and Forrest Meggers. 2020. The Building Data Genome Project 2 energy meter data from the ASHRAE Great Energy Predictor III competition. Scientific Data 7 (Oct. 2020) 368.","DOI":"10.1038\/s41597-020-00712-x"},{"key":"e_1_3_3_1_23_2","volume-title":"Probabilistic Machine Learning: An introduction","author":"Murphy Kevin\u00a0P.","year":"2022","unstructured":"Kevin\u00a0P. Murphy. 2022. Probabilistic Machine Learning: An introduction. MIT Press. probml.ai"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","unstructured":"Aristeidis Mystakidis Paraskevas Koukaras Nikolaos Tsalikidis Dimosthenis Ioannidis and Christos Tjortjis. 2024. Energy Forecasting: A Comprehensive Review of Techniques and Technologies. Energies 17 7 (2024) 1662. 10.3390\/en17071662","DOI":"10.3390\/en17071662"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","unstructured":"Razak Olu-Ajayi Hafiz Alaka Hakeem Owolabi Lukman Akanbi and Sikiru Ganiyu. 2023. Data-Driven Tools for Building Energy Consumption Prediction: A Review. 16 6 (2023) 2574. 10.3390\/en16062574","DOI":"10.3390\/en16062574"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","unstructured":"Razak Olu-Ajayi Hafiz Alaka Ismail Sulaimon Funlade Sunmola and Saheed Ajayi. 2022. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering 45 (2022) 103406. 10.1016\/j.jobe.2021.103406","DOI":"10.1016\/j.jobe.2021.103406"},{"key":"e_1_3_3_1_27_2","unstructured":"F. Pedregosa G. Varoquaux A. Gramfort V. Michel B. Thirion O. Grisel M. Blondel P. Prettenhofer R. Weiss V. Dubourg J. Vanderplas A. Passos D. Cournapeau M. Brucher M. Perrot and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011) 2825\u20132830."},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","unstructured":"Shailendra Singh and Abdulsalam Yassine. 2018. Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting. Energies 11 2 (2018) 452. 10.3390\/en11020452","DOI":"10.3390\/en11020452"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","unstructured":"Sean\u00a0J. Taylor and Benjamin Letham. 2018. Forecasting at Scale. The American Statistician 72 1 (2018) 37\u201345. 10.1080\/00031305.2017.1380080 arXiv:10.1080\/00031305.2017.1380080","DOI":"10.1080\/00031305.2017.1380080"},{"key":"e_1_3_3_1_30_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_1_31_2","unstructured":"Shuhei Watanabe. 2023. Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance. arxiv:https:\/\/arXiv.org\/abs\/2304.11127\u00a0[cs.LG]"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/759"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"publisher","unstructured":"Ziwei Xiao Wenjie Gang Jiaqi Yuan Zhuolun Chen Ji Li Xuan Wang and Xiaomei Feng. 2022. Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning. Energy and Buildings 258 (2022) 111832. 10.1016\/j.enbuild.2022.111832","DOI":"10.1016\/j.enbuild.2022.111832"},{"key":"e_1_3_3_1_34_2","volume-title":"Dive into Deep Learning","author":"Zhang Aston","year":"2023","unstructured":"Aston Zhang, Zachary\u00a0C. Lipton, Mu Li, and Alexander\u00a0J. Smola. 2023. Dive into Deep Learning. Cambridge University Press. https:\/\/D2L.ai."},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.7970649"}],"event":{"name":"E-Energy '25: The 16th ACM International Conference on Future and Sustainable Energy Systems","location":"Rotterdam Netherlands","acronym":"E-Energy '25","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3679240.3734635","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T13:55:43Z","timestamp":1750082143000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3679240.3734635"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,16]]},"references-count":34,"alternative-id":["10.1145\/3679240.3734635","10.1145\/3679240"],"URL":"https:\/\/doi.org\/10.1145\/3679240.3734635","relation":{},"subject":[],"published":{"date-parts":[[2025,6,16]]},"assertion":[{"value":"2025-06-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}