{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:59:25Z","timestamp":1672293565212},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>The need to reduce energy consumption on a global scale has been of high importance during the last years. Research has created methods to make highly accurate forecasts on the energy consumption of buildings and there have been efforts towards the provision of automated forecasting for time series prediction problems. EnForce is a novel system that provides fully automatic forecasting on time series data, referring to the energy consumption of buildings. It uses statistical techniques and deep learning methods to make predictions on univariate or multivariate time series data, so that exogenous factors, such as outside temperature, are taken into account. Moreover, the proposed system provides automatic data preprocessing and, therefore, handles noisy data, with missing values and outliers. EnForce includes full API support and can be used both by experts and non-experts. The proposed demonstration showcases the advantages and technical features of EnForce.<\/jats:p>","DOI":"10.14778\/3476311.3476341","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:43Z","timestamp":1635461323000},"page":"2771-2774","source":"Crossref","is-referenced-by-count":0,"title":["Automated energy consumption forecasting with EnForce"],"prefix":"10.14778","volume":"14","author":[{"given":"Mary","family":"Karatzoglidi","sequence":"first","affiliation":[{"name":"National Tech. Univ. of Athens"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paraskevas","family":"Kerasiotis","sequence":"additional","affiliation":[{"name":"National Tech. Univ. of Athens"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Verena","family":"Kantere","sequence":"additional","affiliation":[{"name":"National Tech. Univ. of Athens"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Automatic time series forecasting: The forecast package for R. Journal of Statistical Software","author":"Khandakar R. J.","year":"2008","unstructured":"Hyndman, R. J. , & Khandakar , Y. ( 2008 ). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. 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