Meeting the ever-growing global energy demand while reducing carbon emissions is one of the most prominent challenges of our era. In this context, efficient control of an operation, service or production process is a key tool to achieve this goal. While there are many opportunities for efficient control within the energy sustainability agenda, this work focuses on domestic space heating systems and intermittent energy resources. This is because in many countries, such as the UK and the US, the domestic sector accounts for more than 20% of the total energy consumption and over 40% of this share is related to space heating. In addition, in recent years, an increasing number of intermittent energy resources, such as photovoltaic systems and wind turbine generators are being integrated into the grid. As such, efficient control of domestic space heating systems and intermittent energy resources can lead to a major reduction in energy consumption and the corresponding CO2 emission. In more detail, domestic space heating automation systems (DHASs) aim to optimize the control process of domestic space heating systems with minimum user-input. Moreover, in the case of electricity-based heating, such systems can also incorporate economic control to exploit the energy buffer that heating loads provide in order to shift the heating consumption according to financial incentives, such as variable electricity import tariffs and/or the availability of cheap electricity coming from house-integrated intermittent energy resources. In the latter case, the financial benefits of economic control can be further amplified in domestic coalitions where a number of houses share their energy generation to minimize the collective energy imported from the grid. Against this background, the first main strand of work in this thesis is to develop a new DHAS, AdaHeat, that overcomes limitations of previous approaches regarding: (i) their efficiency in dealing with the thermal dynamics of houses, (ii) their efficiency in dealing with the inherent uncertainty of the occupancy schedule in domestic settings, (iii) their usability and effectiveness in meeting the user preferences, (iv) their ability to work in conjunction with a diverse range of heating systems, and (v) their ability to efficiently consider economic control in the case of electricity-based heating, exploiting also, for the first time, the aforementioned coalition potential. The backbone of AdaHeat is an adaptive model predictive control approach along with a new general heating schedule planning algorithm based on dynamic programming. In the case of economic control in the presence of house-integrated intermittent energy resources, our planning approach relies on stochastic predictions of the shared intermittent energy resource power output. To this end, we also develop a new adaptive site-specific calibration technique to improve such predictions based on Gaussian process modeling. We present thorough evaluation of the proposed system, and show its effectiveness in terms of Pareto efficiency and usability criteria against state-of-the-art DHASs. We also show that collective economic control, in the presence of house-integrated IERs, can improve heating cost-efficiency by up to 60%, compared to independent economic control, and even more when compared to no economic control. The second strand of work is concerned with increasing the efficiency of intermittent energy resources themselves, through efficient control. In particular, specifically for photovoltaic systems, solar tracking can be used to orient the system towards the greatest possible levels of incoming solar irradiance. This can increase the power output of a photovoltaic system by up to 100%. However, current solar tracking techniques suffer from several drawbacks: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. As such, in this work, we propose PreST; a novel, low-cost and generic solar tracking approach that overcomes the above limitations, utilizing optimal control (proposed for the first time for solar tracking). In particular, our approach is able to calculate appropriate trajectories for efficient and effective day-ahead (predictive) solar tracking, based on available weather forecasts (that can come from on-line providers for free). To this end, we propose a new approximating policy iteration algorithm, suitable for large Markov decision processes, and a novel and generic solar tracking consumption model. Our simulations show that our approach can increase the power output of a photovoltaic system considerably, when compared to standard solar tracking techniques, that can lead to significant monetary gains. As outlined above, apart from their great share in contemporary economies, both domestic space heating systems and intermittent energy resources provide considerable opportunities for energy efficient improvements through efficient control. In this work we exploit this potential and propose respective systems that improve their independent, as well as their interaction, efficiency. This can considerably reduce the respective energy consumption and the corresponding CO2 emission towards fulfilling our goal for an energy sustainable future.

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