Predicting the power output of distributed renewable energy resources within a broad geographical region

In recent years, estimating the power output of inherently intermittent and potentially distributed renewable energy sources has become a major scientific and societal concern. In this paper, we provide an algorithmic framework, along with an interactive web-based tool, to enable short-to-middle term forecasts of photovoltaic (PV) systems and wind generators output. Importantly, we propose a generic PV output estimation method, the backbone of which is a solar irradiance approximation model that incorporates free-to-use, readily available meteorological data coming from online weather stations. The model utilizes non-linear approximation components for turning cloud-coverage into radiation forecasts, such as an MLP neural network with one hidden layer. We present a thorough evaluation of the proposed techniques, and show that they can be successfully employed within a broad geographical region (the Mediterranean belt) and come with specific performance guarantees. Crucially, our methods do not rely on complex and expensive weather models and data, and our web-based tool can be of immediate use to the community as a simulation data acquisition platform.<br/

AdaHeat: A general adaptive intelligent agent for domestic heating control

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.Improving the energy efficiency of domestic heating systems can lead to a major reduction in energy consumption and the corresponding CO2 emissions. To this end, intelligent domestic heating agents (IDHAs) aim to operate domestic heating systems more efficiently with minimum user input. In this work, we propose a new general IDHA that balances heating cost and thermal discomfort in an infinite horizon optimization manner, learns an adaptive thermal model of the system under control on-line and plans a heating schedule that fully exploits the probabilistic occupancy estimates. Importantly, our agent adapts to the user preferences in balancing heating cost and thermal discomfort, as it relies on a single parametrization variable that is learned on-line, and is able to consider a wide range of heating systems typically employed in domestic settings. The backbone of our IDHA is an adaptive model predictive control approach along with a new general planning algorithm that utilizes dynamic programming. We present a thorough evaluation of our approach, and show its effectiveness in terms of Pareto efficiency and usability criteria against state-of-the-art IDHAs. By so doing, we also conduct a comprehensive characterization of existing IDHAs to provide significant insights about their performance in different operational settings

Poster abstract. Applying extended kalman filters to adaptive thermal modelling in homes

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 hom

Efficient control of domestic space heating systems and intermittent energy resources

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

Towards Optimal Solar Tracking: A Dynamic Programming Approach

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. </jats:p

Poster Abstract: Applying Extended Kalman Filters to Adaptive Thermal Modelling in Homes

Abstract 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