by Donatos Stavropoulos (University of Thessaly), Panagiotis Tzimotoudis (University of Thessaly), and Thanasis Korakis (University of Thessaly)
This article explores how integrating Internet of Things (IoT) and smart grid technologies in residential settings can significantly enhance energy efficiency and management, balancing energy demand and supply to create sustainable urban living environments.
The rapid urbanisation and increasing energy demand necessitate innovative solutions for sustainable city living. One promising approach lies in enhancing residential energy efficiency and management through the integration of IoT and smart grid technologies [1]. We have developed an experimental platform – a living lab comprising 50 households – to explore and validate these solutions in real-world settings.
IoT and Smart Grid Integration
The integration of IoT with smart grids forms a robust foundation for managing energy consumption in urban residential areas. This setup facilitates real-time data collection, communication, and control of various devices and systems connected to the grid. By deploying IoT-enabled smart meters, sensors and appliances, cities can monitor and optimise energy use effectively.
In the living lab in Volos, Greece, households are equipped with commercially available sensors and custom gateways based on single-board computers running open-source home automation frameworks. This infrastructure provides a scalable, reliable and practical platform for evaluating new energy management solutions before real-world deployment (see Figure 1).
Figure 1: Integration of IoT and smart grid technologies in residential settings. Hardware components including smart appliances, IoT sensors and smart meters installed within the electrical panels.
Key Components of the Smart Home Testbed
1. IoT Gateways: Serving as the system’s backbone, IoT gateways like the Raspberry Pi 4B integrate with various sensors and devices within the home. These gateways collect data on energy consumption, power usage and environmental conditions, transmitting this data to cloud-based services for analysis. A key characteristic of our custom gateway is its ability to integrate sensors and devices regardless of network access technology and vendor, achieved through the utilisation of open-source frameworks and hardware sniffers.
2. Smart Meters/Plugs and Relays: Installed within electrical panels, smart meters provide real-time energy consumption data. Smart plugs offer submetering capabilities and remote control of connected devices, essential for detailed monitoring and management of household energy usage. Additionally, smart relays are installed within specific circuits to control heavy load devices like electric water heaters.
3. Sensors and Actuators: Various sensors measure indoor and outdoor environmental conditions, while actuators enable control of home devices. Examples include commercial ZigBee temperature and humidity sensors, door contact sensors, motion sensors, and infrared hubs for controlling legacy devices like air conditioners without WiFi capabilities. These components allow for a comprehensive understanding of energy consumption patterns and the implementation of energy-saving measures.
4. Smart Appliances: IoT-enabled appliances, such as washing machines, dryers and dishwashers, can be remotely scheduled to operate during off-peak hours, optimising energy usage and contributing to demand response strategies. In the context of the European-funded project InterConnect [L1], manufacturers including BSH, Miele, and Whirlpool provided WiFi-enabled smart appliances capable of receiving remote control commands to initiate their operation earlier than their scheduled runtime.
Data Interoperability and Use Cases
A critical aspect of the smart home and grid experimental platform is data interoperability, achieved through the use of ontologies such as SAREF (Smart Appliances REFerence ontology) [2]. This framework ensures seamless data exchange and integration across various devices and platforms, supporting use cases that include machine learning, user engagement, and demand response. Within the InterConnect project, we utilised this semantic interoperability framework [L2] to integrate our experimental platform with services developed by other project partners, enabling the effective execution of the applications described below.
Machine Learning and Forecasting: By analysing collected data, machine learning algorithms can predict energy consumption patterns, identify opportunities for efficiency improvements, and optimise load management. Power consumption data is coupled with environmental data from sensors installed in the houses, enhancing the accuracy of the forecasting models. These predictive models help balance the grid and reduce peak demand, significantly contributing to overall energy efficiency. Implemented examples include personalised recommendations that encourage residents to shift their energy usage to times when renewable energy sources (RES) are abundant in the energy mix, thereby reducing their environmental footprint.
User Engagement and Feedback: Engaging residents through dashboards and mobile apps significantly enhances energy management. Users can monitor their energy usage, receive personalised recommendations, and provide feedback, fostering a dynamic interaction that promotes energy-saving behaviours. Tested methodologies included a mobile app that provided recommendations to residents and tracked whether these recommendations were followed. Additionally, the app allowed residents to specify their flexibility preferences, indicating times of the day they were willing to reduce their energy consumption by permitting remote control of their devices. This interactive approach not only encourages responsible energy use but also supports the grid’s demand response strategies.
Demand Response: Demand response, or DR, is a critical component of modern energy management systems. It refers to the ability to adjust the demand for power instead of adjusting the supply. Implementing demand response strategies involves adjusting energy consumption based on real-time data and grid demands. Smart homes equipped with IoT devices can automatically reduce or shift energy usage in response to signals from utility companies, thereby enhancing grid stability and efficiency. During the pilot, two use cases were demonstrated: the first involved real-time device control for emergency situations, while the second offered scheduling capabilities for smart appliances over the next 24 hours to improve load balancing on the grid.
Conclusion and Future Directions
The development of IoT and smart grid technologies in residential settings presents significant potential for creating sustainable cities. By enhancing energy efficiency and management, these technologies can reduce the urban energy footprint and promote sustainable living practices.
Future initiatives aim to expand the experimental platform to include smart office buildings and retail establishments, further integrating IoT devices like air quality sensors and intelligent thermostats. The ultimate goal is to create an experimental platform that closely mirrors real-life scenarios, enabling thorough evaluation and optimisation of smart city solutions.
Links:
[L1] https://www.interconnectproject.eu/
[L2] https://gitlab.inesctec.pt/groups/interconnect-public/-/wikis/home/#interconnect-interoperability-framework
References:
[1] M. Ponce Jara et al., “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, vol. 18, pp. 38–52, 2017.
[2] R. García-Castro et al., “The ETSI SAREF ontology for smart applications: a long path of development and evolution,” Energy Smart Appliances: Applications, Methodologies, and Challenges, 2023.
Please contact:
Donatos Stavropoulos, University of Thessaly, Greece
