Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategyComment: Submitted to Energies - MDPI.co

Distributed environmental control

We present an architecture of distributed, independent control agents designed to work with the Computer Aided System Engineering and Analysis (CASE/A) simulation tool. CASE/A simulates behavior of Environmental Control and Life Support Systems (ECLSS). We describe a lattice of agents capable of distributed sensing and overcoming certain sensor and effector failures. We address how the architecture can achieve the coordinating functions of a hierarchical command structure while maintaining the robustness and flexibility of independent agents. These agents work between the time steps of the CASE/A simulation tool to arrive at command decisions based on the state variables maintained by CASE/A. Control is evaluated according to both effectiveness (e.g., how well temperature was maintained) and resource utilization (the amount of power and materials used)

HVAC SYSTEM REMOTE MONITORING AND DIAGNOSIS OF REFRIGERANT LINE OBSTRUCTION

A heating, ventilation, and air conditioning (HVAC) system of a building includes a refrigerant loop. A monitoring system for the HVAC system includes a monitoring device installed at the building. The monitoring device is configured to measure a first temperature of refrigerant in a refrigerant line located between a filter - drier of the refrigerant loop and an expansion valve of the refrigerant loop. The monitoring system includes a monitoring server, located remotely from the building. The monitoring server is con figured to receive the first temperature and, in response to the first temperature being less than a threshold, generate a refrigerant line restriction advisory. The monitoring server is configured to, in response to the refrigerant line restriction advisory, selectively generate an alert for transmission to at least one of a customer and an HVAC contractor

Agent-based control for decentralised demand side management in the smart grid

Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency)

BUILDING ENVELOPE AND INTERIOR GRADING SYSTEMS AND METHODS

A difference module determines differences between an outdoor ambient temperature and an indoor temperature, determines a first average of the differences , and determines a second average of the differences. A storing module stores a first data point, the first data point including the first average and a first total run time of a heating, ventilation, and/or air conditioning (HVAC) system, and stores a second data point, the second data point including the second average and a second total run time of the HVAC system. A fitting module fits a line to the first and second data points. An envelope grading module generates a grade for an exterior envelope of a building based on a first characteristic of the line. An interior grading module generates a grade for an interior of the building based on a second characteristic of the line. A reporting module generates a displayable report for the building including the grade of the exterior envelope and the grade of the interior of the building

HEAT PUMP AND AIR CONDITIONING GRADING SYSTEMS AND METHODS

An expectation module determines an expected average power consumption of a heat pump for a predetermined period as a function of indoor and outdoor temperatures of the building during the predetermined period. A difference module determines a power difference between an average power consumption of the heat pump during the predeter mined period and the expected average power consumption of the heat pump for the predetermined period. A grade determination module determines a grade of the heat pump for the predetermined period based on the power difference of the predetermined period. A reporting module generates a displayable report including the grade of the heat pump for the predetermined period

Low carbon housing: lessons from Elm Tree Mews

This report sets out the findings from a low carbon housing trial at Elm Tree Mews, York, and discusses the technical and policy issues that arise from it. The Government has set an ambitious target for all new housing to be zero carbon by 2016. With the application of good insulation, improved efficiencies and renewable energy, this is theoretically possible. However, there is growing concern that, in practice, even existing carbon standards are not being achieved and that this performance gap has the potential to undermine zero carbon housing policy. The report seeks to address these concerns through the detailed evaluation of a low carbon development at Elm Tree Mews. The report: * evaluates the energy/carbon performance of the dwellings prior to occupation and in use; * analyses the procurement, design and construction processes that give rise to the performance achieved; * explores the resident experience; * draws out lessons for the development of zero carbon housing and the implications for government policy; and * proposes a programme for change, designed to close the performance gap