On the Energy Consumption Forecasting of Data Centers Based on Weather Conditions: Remote Sensing and Machine Learning Approach
2018 (English)In: 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), IEEE , 2018, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]
The energy consumption of Data Centers (DCs) is a very important figure for the telecommunications operators, not only in terms of cost, but also in terms of operational reliability. A reliable weather forecast would result in a more efficient management of the available energy and would make it easier to take advantage of the modern types of power-grid based on renewable energy resources. In this paper, we exploit the capabilities provided by the FIESTA-IoT platform in order to investigate the correlation between the weather conditions and the energy consumption in DCs. Then, by using multi-variable linear regression process we model this correlation between the energy consumption and the dominant weather condition parameters in order to effectively forecast the energy consumption based on the weather forecast. This procedure could be part of a wider resources optimization process in the core network towards an end-to-end (e2e) access/core network optimization of resources utilization. We have validated our results through live measurements from the RealDC testbed. Results from our proposed approach indicate that forecasting of energy consumption based on weather conditions could help not only DC operators in managing their cooling systems and power usage, but also electricity companies in optimizing their power distribution systems.
Place, publisher, year, edition, pages
IEEE , 2018. p. 1-6
Keywords [en]
computer centres, energy consumption, Internet of Things, learning (artificial intelligence), optimisation, power distribution, power grids, regression analysis, remote sensing, weather forecasting, energy consumption forecasting, data centers, weather conditions, machine learning approach, reliable weather forecast, renewable energy resources, dominant weather condition parameters, DC, telecommunications operators, operational reliability, power-grid, realDC testbed, e2e access-core network optimization, end-to-end access-core network optimization, multivariable linear regression process, FIESTA-IoT platform, Cooling, Predictive models, Data models, FIESTA-IoT, energy efficiency, weather forecast, linear regression, machine learning, power grid
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-174241DOI: 10.1109/CSNDSP.2018.8471785OAI: oai:DiVA.org:liu-174241DiVA, id: diva2:1537845
Conference
2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP)
Note
Funding agencies: Part of this work has been funded by the FIESTA-IoT project, with GA number: 643943. The REAL-DC testbed has been utilized for gathering the data that have been used in this work. Georgios Smpokos is a researcher at CYTA-Hellas and funded by the WiVi-2020 project. Mohamed Elshatshat contributed to this work during his secondment to CYTA-Hellas from ICS-FORTH and funded by the WiVi-2020 project. The WiVi-2020 project has received funding from the European Unions Seventh Framework Programme (FP7/2007-2013) under grant agreement no 324515 and from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642743.
2021-03-172021-03-172021-03-17Bibliographically approved
In thesis