English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The energy consumption of Data Centers (DCs) is a very important figure forthe telecommunications operators, not only in terms of cost, but also in termsof operational reliability. A relation between the energy consumption and theweather conditions would indicate that weather forecast models could be usedfor predicting energy consumption of DCs. A reliable forecast would result in amore efficient management of the available energy and would make it easier totake advantage of the modern types of power-grid based on renewable energyresources. In this ,paper, we exploit the capabilities provided by theFIESTA-IoT platform in order to investigate the correlation between the weatherconditions and the energy consumption in DCs. Then, by using multi-variablelinear regression process, we model this correlation between the energyconsumption and the dominant weather conditions parameters in order toeffectively forecast the energy consumption based on the weather forecast. Wehave validated our results through live measurements from the RealDC testbed.Results from our proposed approach indicate that forecasting of energyconsumption based on weather conditions could help not only DC operators inmanaging their cooling systems and power usage, but also electricity companiesin optimizing their power distribution systems.

On the Energy Consumption Forecasting of Data Centers Based on Weather Conditions: Remote Sensing and Machine Learning Approach