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It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small\u2010scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)\u2010SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA\u2010SVR is compared with the other four models, respectively, are the ARIMA, ARIMA\u2010GBR (gradient boosting regression), LSTM (long short\u2010term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA\u2010SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA\u2010SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.<\/jats:p>","DOI":"10.1155\/2021\/6610273","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T19:37:40Z","timestamp":1618256260000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Appling an Improved Method Based on ARIMA Model to Predict the Short\u2010Term Electricity Consumption Transmitted by the Internet of Things (IoT)"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5687-3973","authenticated-orcid":false,"given":"Ni","family":"Guo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7663-278X","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Manli","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Haoyue","family":"Jin","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118100"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13143645"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-8352(98)00066-7"},{"key":"e_1_2_10_4_2","unstructured":"TandonH. 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Time series analysis and forecasting of COVID-19 cases using LSTM and ARIMA models 2020 https:\/\/arxiv.org\/abs\/2006.13852."},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2020.02.117"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13112924"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117200"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2970165"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118477"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115383"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2014.03.033"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117948"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/2475-8876.12135"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13010010"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.114985"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-020-02455-4"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13143722"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.3390\/en13092242"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.117159"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1975.10480264"},{"key":"e_1_2_10_22_2","first-page":"281","article-title":"Support vector method for function approximation, regression estimation and signal processing","volume":"9","author":"Vapnik V.","year":"2008","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4302-5990-9_4"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)EY.1943-7897.0000405"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/6610273.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/6610273.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6610273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:29:22Z","timestamp":1723030162000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6610273"}},"subtitle":[],"editor":[{"given":"Chi-Hua","family":"Chen","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6610273"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6610273","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"value":"1530-8669","type":"print"},{"value":"1530-8677","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-12-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6610273"}}