Abstract

Energy efficiency is a key challenge for building sustainable societies. Due to growing populations, increasing incomes and the industrialization of developing countries, the world primary energy consumption is expected to increase annually by 1.6%. This scenario raises issues related to the increasing scarcity of natural resources, the accelerating pollution of the environment, and the looming threat of global climate change. In this paper we introduce a new and original approach to predict next week energy consumption based on human dynamics analysis derived out of the anonymized and aggregated telecom data, which is processed from GSM network call data records (CDRs). We introduce an original problem statement, analyze regularities of the source data, provide insight on the original feature extraction method and discuss peculiarities of the regression models applicable for this big data problem. The proposed solution could act on energy producers/distributors as an essential aid to smart meters data for making better decisions in reducing total primary energy consumption by limiting energy production when the demand is not predicted, reducing energy distribution costs by efficient buy-side planning in time and providing insights for peak load planning in geographic space.

Highlights

  • Energy efficiency is a key challenge for building sustainable societies

  • Our results prove that people dynamics, extracted from aggregated and anonymized mobile phone data, are good proxies for modeling energy consumption

  • Assuming that electrical potential, measured in V is standardized in Trentino province and given the same timeframes for the analysis, the electric energy consumption prediction task reduces to predicting electric current measured in Ampere per each time frame per each line ID

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Summary

Introduction

Energy efficiency is a key challenge for building sustainable societies. %. Due to growing populations, increasing incomes and the industrialization of developing countries, the world primary energy consumption is expected to increase annually by . In order to improve the efficiency of the supply systems and to reduce the amount of energy consumption, a critical step is to understand energy needs at relatively high spatial and temporal resolution. An accurate prediction of energy demands could provide useful information to make decisions on energy generation and purchase. An accurate prediction would have a significant impact on preventing overloading and allowing

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