WP1: Energy Services and Demand

WP Leader: Per S. Nielsen, per.s.nielsen@smart-cities-centre.org

Objective: Characterize and model energy services and demand in cities, and their geographical and temporal variations.


WP1.1 Examine existing data, models and demonstration projects to identify forms of energy demand, its variations and primary characteristics (magnitude, dynamics, uncertainty, potential for flexibility and storage).
WP1.2 Establish a data-hub for energy related data to facilitate systems integration studies in cities through easily accessible data. Data security will be of the highest priority here, guarding against unauthorised access, manipulation of sensitive data, and privacy breaches.
WP1.3 Develop models of energy demand at the component (building) level, primarily using energy informatics based techniques (combining physical and statistical information) and focussing on the role of intelligent demand and prosumers in the future energy system.
WP1.4 Develop tools for identifying energy performance characteristics of future buildings. These tools can be used to generate building ratings, and to identify and screen for optimal opportunities for energy savings and improved consumer flexibility.
WP1.5 Examine the relationship between consumption characteristics and profiles, and socio-economic data.

PhD projects

Analysis of high frequency smart meter data

PhD student: Alexander Martin Tureczek, martin.a.tureczek@smart-cities-centre.org

By the end of 2020, all electricity consumers in Denmark will have a smart meter installed for measuring consumption. This project is concerned with the analysis and characterization of energy end-user consumption patterns using data from the smart electricity meters. Applying the consumption data for statistical classification and description of electricity and district heat consumption patterns will deepen our understanding of consumption patterns. Knowledge of consumption patterns can help utilities to optimize their current production and identify flexibility in the electricity grid. Electrification of the transportation sector has a high priority and will result in a significant increase in electricity demand in the future and a need for smarter consumption of electricity. Understanding electricity consumption is the key in optimizing the future electricity grid and production. AffaldVarme Aarhus and SydEnergi are demo cases in this WP and supply smart meter data for district heat and electricity for analysis. Machine learning will play an integral part of the analysis.

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