Energy efficiency in the smart home: a model to evaluate the economic and environmental benefits of IoT solutions

In the European Union, residential and commercial buildings together account for slightly less than 40% of primary energy consumption, emitting around 36% of EU greenhouse gas (GHG) emissions. Since the residential sector is one of the main contributors to the country energy balance, countermeasures intended to reduce consumption need to be taken. In this context, new opportunities emerge from the Internet of Things devices for the so-called “smart home”. Indeed, energy consumption can be monitored in detail (e.g. lighting, heating, watering), and improved by mean of smart devices. Among all, the present work focuses on heating, which contributes the most to carbon dioxide (CO2) emissions and leads to high billing costs. Two IoT heating solutions have been considered, i.e. the smart programmable thermostat, which can be remotely controlled, and the smart learning thermostat, which in addition uses artificial intelligence algorithms in order to “learn” from the behaviour of the people at home and regulate the optimal temperature consistently. More specifically, an analytical model has been developed to quantify the potential benefits of smart heating solutions, both in terms of economic and environmental savings. The model takes into consideration both the house features (e.g. thermal transmittance, dispersing surface) and the consumers’ profile. It can be applied to every house, as long as this is supplied by a radiator heating system. From the application of the model, it emerges that energy consumption – both in terms of CO2 emissions and cost – can be reduced from 5% up to about 30%. Anyway, results depend on the user profile and, in particular, on the time spent at home: the more unpredictable the user profile, the higher the savings.