Abstract
Consumers lie at the epicenter of smart grids, since their activities account for a large portion of the total energy demand. Therefore, utility companies, governmental agencies, and various other entities with environmental concerns aim at lowering and shaping energy consumption patterns to achieve peak load reduction, load smoothing, and hence carbon emission curtailment. In this survey paper, we present an overview of approaches for engaging smart grid consumers and for providing them with information, motivation, and recommendations for energy efficiency through mobile apps. Our focus is to bring machine learning approaches closer to smart grid mobile apps so as to optimally manage consumer flexibility and enhance energy savings through detailed consumer profiling and modeling, since an increasing amount of energy consumer data is becoming available. A novel survey and analysis of prior work in the area is conducted in order to identify gaps from this perspective. We consider both recent research project outputs and commercial products and we discuss various aspects of the designs, such as state-of-the-art technologies, extrinsic and intrinsic motivation techniques, gamification, consumer profiling, and the role of machine learning and recommender systems in this context. Furthermore, different mobile apps are presented and compared based on the most important features that affect consumer energy efficiency and sustainability, such as data visualization, gamification, flexibility, consumer profiling methods, feedback mechanisms, recommendations, social media, and machine learning integration. The main goal of this work is to identify how mobile apps incorporate these features to engage energy consumers in energy-efficient behavior, assess the current state-of-the-art in the area, and highlight future research directions.
Highlights
The smart grid ecosystem is growing rapidly during the last decades, contributing to the efforts of environmental green transition, energy efficiency, and reduction of carbon levels
Consumers comprise the main actors directly involved in the electricity grid [2], while buildings are responsible for about one third of the global energy consumption [3], with residential buildings accounting for 22% of the total energy demand worldwide [4]
We present a review of existing work on mobile apps for the smart grid ecosystem and residential energy efficiency
Summary
The smart grid ecosystem is growing rapidly during the last decades, contributing to the efforts of environmental green transition, energy efficiency, and reduction of carbon levels. Machine learning techniques can be used to capture consumer profile information from historical data, e.g. past responses to similar recommendations from the mobile app, in order to make a more efficient task allocation between users in terms of costs and impact [28]. Such crowdsensing approaches can be generalized and applied to mobile applications for energy efficiency, where energy-related tips and recommendations are presented to the users to reduce their energy consumption [27]. Environmental impact presentation informs users with statistics and reports on how their activities affected the environment and other impact that their saved energy had, motivating them to engage with the app even more in order to increase their contribution
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