This is a follow-up sensitivity study on r-mode gravitational wave signals from newborn neutron stars illustrating the applicability of machine learning algorithms for the detection of long-lived gravitational-wave transients. In this sensitivity study we examine three machine learning algorithms (MLAs): artificial neural networks (ANNs), support vector machines (SVMs) and constrained subspace classifiers (CSCs). The objective of this study is to compare the detection efficiencies that MLAs can achieve to the efficiency of the conventional (seedless clustering) detection algorithm discussed in an earlier paper. Comparisons are made using 2 distinct r-mode waveforms. For the training of the MLAs we assumed that some information about the distance to the source is given so that the training was performed over distance ranges not wider than half an order of magnitude. The results of this study suggest that we can use the machine learning algorithms as part of an investigative stage in the pipeline that would be able to provide very fast and solid triggers for further, and more intense, investigation.

Over the past few years, domestic heating automation systems (DHASs) that optimize the domestic space heating control 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 heating loads 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).

A key challenge for intelligent domestic heating systems is to obtain sufficient knowledge of the thermal dynamics of the home to build an adaptive thermal model. We present a study where stochastic grey-box modelling is used to develop thermal models and an extended Kalman filter is used for parameter estimation for a room in a family home

The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (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. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on-line providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.