Tandem Project

Energy Consumption and Saving Potentials

Research Project

Numerous studies report the considerable energy saving potential existing at the demand-side in many European and Arab countries including Algeria and Germany. Demand-side energy efficiency policies are at the heart of the ongoing energy transformation. 

Although, fine-grained indicators, which enable regularly track changes in energy demand and provide real-time energy statistics does not yet exist, recent energy efficiency policies are based on official statistics collected from other but less reliable resources. By developing such sets of indicators, this tandem projects aims to close this gap and thereby contribute to developing novel energy efficient methods that are useful for defining governmental policies. 

In addition to enhancing the policy-use of energy statistics, the AGYA members Prof. Dr. Marc Ringel, HFWU - Nuertingen-Geislingen University, Germany and Dr. Djamel Djenouri, University of the West of England, Bristol, U.K. look at automated read-outs of energy data to track demand side changes. They also conduct an in-depth economic-statistical screening including energy balances in Germany and Algeria. The results help to assess the impact of energy efficiency policies on relevant climate, energy, and economic aggregates. Moreover, they explore the use of cutting-edge technologies such as deep learning and the Internet of Things (IoT) to enable real-time tracking of demand changes and accurate estimation. This is in line with energy metrics and statistics that can define new approaches to energy conservation and ultimately impact energy transformation policies.

Scetch of houses in different colours


Disciplines Involved
Economics, Engineering, Political Science
Project Title
Energy Consumption and Saving Potentials: Advanced Technology Transformations for Privacy-Aware Data Tracking on the Demand-Side
Funding Scheme
Tandem Project
Countries Involved
Germany, Algeria, United Kingdom, Belgium
AGYA Publication
Towards Energy Efficient Clustering in Wireless Sensor Networks: A Comprehensive Review