Over the past few years, domestic heating automation systems (DHASs) that optimize the domestic space heating con-
trol process with minimum user-input, utilizing appropriate occupancy prediction technology, have emerged as commercial
products (e.g, the smart thermostats from Nest and Honeywell). At the same time, many houses are being equipped with,
potentially grid-connected, intermittent energy resources (IERs), such as rooftop photovoltaic systems and /or small wind
turbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than the
price of buying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASs
need to incorporate advanced economic control (AEC). Such AEC can exploit the energy buffer that heat in gloads provide,in order to shift the consumption of electricity-based heating systems to follow the intermittent energy generation of the house.
By so doing, the energy imported from the grid can be minimized and considerable monetary gains for the household can be achieved, without affecting the occupants’ schedule. These benefits can be amplified still further in domestic coalitions,
where a number of houses come together and share their IER generation to minimize their cumulative grid energy import.
Given the above, in this work we extend a state-of-the-art DHAS, to propose AdaHeat+, a practical DHAS, that, for the
first time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes complete
with an allocation mechanism to share the coalition gains. Importantly, we propose an effective heuristic heating schedule
planning approach for collective AEC which: (i) has a complexity that scales in a linear and parallelizable manner with the
coalition size,and (ii) enables AdaHeat+ to handle the distinct preferences, in balancing heating cost and thermal discomfort,
of the households. Our approach relies on stochastic IER power output predictions. In this context, we propose a simple
and effective formulation for the site-specific calibration of such predictions based on adaptive Gaussian process modeling.
Finally, we demonstrate the effectiveness of AdaHeat+
through real data evaluation, to show that collective AEC can improve
heating cost-efficiency by up to 60%, compared to independent AEC (and even more when compared to no-AEC).