Abstract
This paper presents the concept of an interactive and comprehensive platform based on an advanced metering infrastructure for exchanging information on energy consumption (and consequently on energy efficiency) in an urban and industrial environment which can serve as a powerful tool for monitoring progress in the transition toward a low carbon society. The proposed concept aims at supporting energy utilities in optimizing the energy performance of both supply and demand side aspects of their work and has the potential to fill a gap and help in harmonization of interests between the energy utilities, energy service providers, local energy agencies and citizens. The proposed concept should be realized as a platform with modular architecture, allowing future expansion of the user’s portfolio and inventory management (new energy efficiency measures, technologies, different industries, urban districts and regions).
Highlights
The transition toward a low carbon society has become a primary climate mitigation activity in many countries
Several studies have confirmed that the decisions and concrete actions of individuals have to be considered in the effort to impact end-user energy behaviour as they can have greater power and effect than the ability of the municipal administration to direct in their actions [1, 2]
According to [5], user behaviour is at least as important as building physics when it comes to energy consumption for heating, while electricity consumption for lighting and appliances is more dependent on user behaviour than on energy efficiency of appliances
Summary
The transition toward a low carbon society has become a primary climate mitigation activity in many countries. The proposed concept comprises five interlinked functionalities with the following features: End-user consumption data acquisition: interconnection with smart meters, where consumption data is acquired at 1 minute intervals in the case of electricity, or 1 hour intervals in the case of heat, which should enable development of pattern recognition algorithms; Pattern recognition algorithms: discovering patterns of energy and appliance usage to identify unnecessary consumption, to find opportunities for change, and to set targets for improvement; Context-sensitive processing: enriching the consumption data with information already stored in the knowledge repository of the utility (location, end-user profile, current use/ installation of de-centralized renewable energy sources and CHP, etc.); Consumption feedback: elaborate individual energy profiles (combining individual end-user data) to contextualize consumption feedback (benchmarking, awareness raising, warnings and alarming) This includes communicating information about energy consumption effectively to the end-user to increase awareness of the relevance of one’s behaviour to energy consumption, and awareness of one’s possibilities to influence personal behaviour; Decision support services: introducing end-user energy consumption profiles and end-user behavioural parameters on the principles of Game Theory into the utility decision process to support the utility’s DSM centre and DRS.
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