{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T10:46:52Z","timestamp":1769942812601,"version":"3.49.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Nowadays, the fast rate of technological advances, such as cloud computing, has led to the rapid growth of the Data Center (DC) market as well as their power consumption. Therefore, DC power management has become increasingly important. While power forecasting can greatly help DC power management and reduce energy consumption and cost. Power forecasting predicts the potential energy failures or sudden fluctuations in power intake from utility grid. However, it is hard especially when variable renewable energies (RE) as well as free cooling such as air economizers are also used. Geo-distributed DCs face an even harder issue: since in addition to local conditions, the overall status of the entire system of collaborating DCs should also be considered. The conventional approach to forecast power consumption in such complicated cases is to construct a closed form formula for power. This is a tedious task that not only needs expert knowledge of how each single cooling or RE system works, but also needs to be done individually for each DC and repeated all over again for each new DC or change of equipment. One alternative is to use machine learning so as to learn over time how the system consumes power in varying conditions of weather, workload, and internal structure in multiple geo-distributed locations. However, due to the wide range of effective features as well as trade-off between the accuracy and processing overhead, one important issue is to obtain an optimal set of more influential features. In this study, we analyze the correlation among geo-distributed DC power patterns with their weather parameters (based on different DC situations and infrastructure) and extract a set of influential features. Afterward, we apply the obtained features to provide a power consumption forecasting model that predict the power pattern of each collaborating DC in a cloud. Our experimental results show that the proposed prediction model for geo-distributed DCs reaches the accuracy of 87.2%.<\/jats:p>","DOI":"10.1186\/s40537-020-0284-2","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T16:02:53Z","timestamp":1580313773000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Learning-based power prediction for geo-distributed Data Centers: weather parameter analysis"],"prefix":"10.1186","volume":"7","author":[{"given":"Somayyeh","family":"Taheri","sequence":"first","affiliation":[]},{"given":"Maziar","family":"Goudarzi","sequence":"additional","affiliation":[]},{"given":"Osamu","family":"Yoshie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,29]]},"reference":[{"key":"284_CR1","unstructured":"Data Center Dynamic. The future of data centers is green. https:\/\/www.datacenterdynamics.com\/opinions\/future-data-centers-green\/. 2019."},{"key":"284_CR2","unstructured":"Globe Newswire: Research and Market: Green Data Center Market\u2014growth, trends, and forecast (2019\u20132024). https:\/\/www.globenewswire.com\/news-release\/2019\/06\/28\/1875927\/0\/en\/Green-Data-Centers-Worldwide-Market-Insight-Report-2019-2024\/Power-Segment-to-Hold-a-Significant-Market-Share.html. 2019."},{"key":"284_CR3","doi-asserted-by":"crossref","unstructured":"Shi Y, Xu B, Wang D, Zhang B. Leveraging energy storage to optimize data center electricity cost in emerging power markets. In: 7th international conference on future energy systems; 2016.","DOI":"10.1145\/2934328.2934346"},{"key":"284_CR4","volume-title":"Energy efficiency in large scale distributed systems","author":"H Guler","year":"2013","unstructured":"Guler H, Cambazoglu B, Ozkasap O. Cutting down the energy cost of geographically distributed cloud DCs. Energy efficiency in large scale distributed systems. Berlin: Springer; 2013."},{"key":"284_CR5","doi-asserted-by":"crossref","unstructured":"Li Y, Wen Y, Zhang J. Learning-based power prediction for data centre operations via deep neural networks. In: 5th international workshop on energy efficient data centres; 2016.","DOI":"10.1145\/2940679.2940685"},{"key":"284_CR6","doi-asserted-by":"crossref","unstructured":"Smpokos G, Elshatshat MA, Iliopoulos I. On the energy consumption forecasting of data centers based on weather conditions: remote sensing and machine learning approach. In: 11th international symposium on communication systems, networks and digital signal processing (CSNDSP); 2018.","DOI":"10.1109\/CSNDSP.2018.8471785"},{"key":"284_CR7","doi-asserted-by":"crossref","unstructured":"Hsu YF, Matsuda K, Matsuoka M. Self-aware workload forecasting in data center power prediction. In: 18th IEEE\/ACM international symposium on cluster, cloud and grid computing (CCGRID); 2018.","DOI":"10.1109\/CCGRID.2018.00047"},{"key":"284_CR8","doi-asserted-by":"crossref","unstructured":"Liu N, Lin X, Wang Y. Data center power management for regulation service using neural network-based power prediction. In: 18th international symposium on quality electronic design (ISQED); 2017.","DOI":"10.1109\/ISQED.2017.7918343"},{"key":"284_CR9","unstructured":"Canada historical climate data. http:\/\/climate.weather.gc.ca\/. 2019."},{"key":"284_CR10","unstructured":"Canada weather stats. http:\/\/www.weatherstats.ca\/. 2019."},{"key":"284_CR11","unstructured":"Google cluster data. https:\/\/github.com\/google\/cluster-data. 2018."},{"key":"284_CR12","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1057\/jors.2014.103","volume":"66","author":"C Tofallis","year":"2015","unstructured":"Tofallis C. A better measure of relative prediction accuracy for model selection and model estimation. J Oper Res Soc. 2015;66:1352\u201362.","journal-title":"J Oper Res Soc"},{"key":"284_CR13","unstructured":"Kingma DP, Adam JB. A method for stochastic optimization. In: 3rd international conference for learning representations; 2015."},{"key":"284_CR14","unstructured":"PVWatts calculator. http:\/\/pvwatts.nrel.gov\/pvwatts. 2019."},{"key":"284_CR15","doi-asserted-by":"crossref","unstructured":"Li C, Qouneh A, Li T. ISwitch: coordinating and optimizing renewable energy powered server clusters. In: 39th annual international symposium on computer architecture (ISCA); 2012. p. 512\u201323.","DOI":"10.1145\/2366231.2337218"},{"key":"284_CR16","unstructured":"Facebook public dashboard for real-time PUE. https:\/\/www.fbpuewue.com\/prineville. 2019."},{"key":"284_CR17","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/978-3-540-92910-9_14","volume-title":"Handbook of natural computing","author":"Zhang G Peter","year":"2012","unstructured":"Peter Zhang G. Neural networks for time-series forecasting. Handbook of natural computing. Berlin: Springer; 2012. p. 461\u201377."}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-0284-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s40537-020-0284-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-0284-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:08:40Z","timestamp":1611792520000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-020-0284-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,29]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["284"],"URL":"https:\/\/doi.org\/10.1186\/s40537-020-0284-2","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,29]]},"assertion":[{"value":"18 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}