Efficient energy planning is a key feature for the future smart cities. The real-time optimization of the energy distribution and storage is the real added value for smart grid and cities. However, the available energy providers' infrastructures are not able to estimate and predict real-time fluctuation of the energy demand and are not scalable enough to integrate, with low cost and effort, hardware elements able to estimate energy demand in real-time. The solution proposed in this paper exploit heterogeneous big data sources to forecast in real-time energy demands without requiring physical interventions on the energy providers' infrastructures. The proposed approach is mainly based on the use of statistical models and cellular network big data to estimate in advance energy demand without observing the actual behaviour of the energy network. Distributor System Operators can use these estimations to self-manage the energy demand, distribution and storage in real-time, without any user intervention. The approach has been extensively validated in a real world case study for the Milan city, in the production infrastructure of Vodafone Italy and with all the Vodafone Mobile Users, and the quality of the probabilistic models in forecasting energy consumption is really promising.

Big data from cellular networks: How to estimate energy demand at real-time

TOSI, DAVIDE;
2015

Abstract

Efficient energy planning is a key feature for the future smart cities. The real-time optimization of the energy distribution and storage is the real added value for smart grid and cities. However, the available energy providers' infrastructures are not able to estimate and predict real-time fluctuation of the energy demand and are not scalable enough to integrate, with low cost and effort, hardware elements able to estimate energy demand in real-time. The solution proposed in this paper exploit heterogeneous big data sources to forecast in real-time energy demands without requiring physical interventions on the energy providers' infrastructures. The proposed approach is mainly based on the use of statistical models and cellular network big data to estimate in advance energy demand without observing the actual behaviour of the energy network. Distributor System Operators can use these estimations to self-manage the energy demand, distribution and storage in real-time, without any user intervention. The approach has been extensively validated in a real world case study for the Milan city, in the production infrastructure of Vodafone Italy and with all the Vodafone Mobile Users, and the quality of the probabilistic models in forecasting energy consumption is really promising.
9781467382731
9781467382731
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11383/2049195
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