Joint Model Predictive Control of Electric and Heating Resources in a Smart Building

The new challenge in power systems design and operation is to organize and control smart micro grids supplying aggregation of users and special loads as electric vehicles charging stations. The presence of renewable and storage can help the optimal operation only if a good control manages all the elements of the grid. New models of green buildings and energy communities are proposed. For a real application they need an appropriate and advanced power system equipped with a building automation control system. This article presents an economic model predictive control approach to the problem of managing the electric and heating resources in a smart building in a coordinated way, for the purpose of achieving in real time nearly zero energy consumption and automated participation to demand response programs. The proposed control, leveraging a mixed integer quadratic programming problem, allows to meet manifold thermal and electric users' requirements and react to inbound demand response signals, while still guaranteeing stable operation of the building's electric and thermal storage equipment. The simulation results, performed for a real case study in Italy, highlight the peculiarities of the proposed approach in the joint handling of electric and thermal building flexibility

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field

Efficiency Gains from "What"-Flexibility in Climate Policy: An Integrated CGE Assessment

We investigate the importance of ?what?-flexibility on top of ?where?- and ?when?-flexibility for alternative emission control schemes that prescribe long-term temperature targets and eventually impose additional constraints on the rate of temperature change. We find that ?what?-flexibility substantially reduces the compliance costs under alternative emission control schemes. When comparing policies that simply involve long-term temperature targets against more stringent strategies that include additional constraints on the rate of temperature increase, it turns out that the latter involve huge additional costs. These costs may be interpreted as additional insurance payments if damages should not only dependent on absolute temperature change but also on the rate of temperature change. --Climate policy,Integrated Assessment,What-flexibility

Heating controls: International evidence base and policy experiences

This report presents a synthesis in the form of narrative summaries of the international evidence base and policy experiences on heating controls in the domestic sector. The research builds on the former Department of Energy and Climate Change (DECC) commissioned (systematic) scoping review of the UK evidence on heating controls published in 2016 (Lomas et al., 2016), and the Rapid Evidence Assessment of smarter heating controls published in 2014 (Munton et al., 2014). The report consists of two parts. Part 1 involves a (systematic) scoping review of the international evidence base on the energy savings, cost-effectiveness and usability of heating controls in the domestic sector. Part 2 contains the findings from an analysis of the policy experiences of other countries

Case studies of mental models in home heat control: searching for feedback, valve, timer and switch theories

An intergroup case study was undertaken to determine if: 1) There exist distinct mental models of home heating function, that differ significantly from the actual functioning of UK heating systems; and 2) Mental models of thermostat function can be categorized according to Kempton’s (1986) valve and feedback shared theories, and others from the literature. Distinct, inaccurate mental models of the heating system, as well as thermostat devices in isolation, were described. It was possible to categorise thermostat models by Kempton’s (1986) feedback shared theory, but other theories proved ambiguous. Alternate control devices could be categorized by Timer (Norman, 2002) and Switch (Peffer et al., 2011) theories. The need to consider the mental models of the heating system in terms of an integrated set of control devices, and to consider user’s goals and expectations of the system benefit, was highlighted. The value of discovering shared theories, and understanding user mental models, of home heating, are discussed with reference to their present day relevance for reducing energy consumption