Design of an Online Optimisation Tool for Smart Home Heating Control

The performance of model predictive smart home heating control (SHHC) heavily depends on the accuracy of the initial setup for individual building characteristics. Since owners or renters of residential buildings are predominantly not experts, users’ acceptance of SHHC requires ease of use in the setup and minimal user intervention (e.g. only declaration of preferences), but at the same time high reliability of the initial parameter settings and flexibility to handle different preferences. In contrast, the training time of self-learning SHHC (e.g. based on artificial neural networks) to reach a reliable control status could conflict with the users’ request for comfortable heating from the very beginning. Dealing with this trade-off, this paper follows the tradition of design science research and presents a prototype of an online optimisation tool (OOT) for SHHC. The OOT is multi objective (e.g. minimising lifecycle energy (cost) or carbon emissions) under constraints such as thermal comfort. While the OOT is based on a discrete dynamic model, its self-adaptation is accelerated by a database of physically simulated characteristic buildings, which allows parameter setting at the beginning by a similarity measurement. The OOT artefact provides a base for empirically testing advantages of different SHHC design alternatives

Lessons learnt from design, off-site construction and performance analysis of deep energy retrofit of residential buildings

The article introduces the process of deep energy retrofit carried out on a residential building in the UK, using a ‘TCosy’ approach in which the existing building is completely surrounded by a new thermal envelope. It reports on the entire process, from establishing the characteristics of the existing building, carrying out design simulations, documenting the off- site manufacture and on-site installation, and carrying out instrumental monitoring, occupant studies and performance evaluation. Multi-objective optimisation is used throughout the process, for establishing the characteristics of the building before the retrofit, conducting the design simulations, and evaluating the success of the completed retrofit. Building physics parameters before and after retrofit are evaluated in an innovative way through simulation of dynamic heating tests with calibrated models, and the method can be used as quality control measure in future retrofit programmes. New insights are provided into retrofit economics in the context of occupants’ health and wellbeing improvements. The wide scope of the lessons learnt can be instrumental in the creation of continuing professional development programmes, university courses, and public education that raises awareness and demand. These lessons can also be valuable for development of new funding schemes that address the outstanding challenges and the need for updating technical reference material, informing policy and building regulations.Peer reviewedFinal Published versio

Challenges in Energy Awareness: a Swedish case of heating consumption in households

An efficient and sustainable energy system is an important factor when minimising the environmental impact caused by the cities. We have worked with questions on how to construct a more direct connection between customers-­‐citizens and a provider of district heating for negotiating notions of comfort in relation to heating and hot tap water use. In this paper we present visualisation concepts of such connections and reflect on the outcomes in terms of the type of data needed for sustainability assessment, as well as the methods explored for channelling information on individual consumption and environmental impact between customers and the provider of district heating. We have defined challenges in sustainable design for consumer behaviour change in the case of reducing heat and hot water consumption in individual households: (1) The problematic relation between individual behaviour steering and system level district heating, (2) The complexity of environmental impact as indicator for behaviour change, and (3) Ethical considerations concerning the role of the designer

Case study based approach to integration of sustainable design analysis, performance and building information modelling

This paper presents a case study based research of both the method and technology for integration of sustainable design analysis (SDA) and building information modelling (BIM) within smart built environments (SBE). Level 3 BIM federation and integration challenges are recognised and improvements suggested, including issues with combining geometry and managing attribute data. The research defines SDA as rapid and quantifiable analysis of diverse sustainable alternatives and ‘what if’ scenarios posed by a design team and client during the early stages of the project, where the benefits of correct decisions can significantly exceed the actual investment required. The SDA concept and BIM integration findings are explained through a convergence from conceptualisation to calculation stages, emphasising the importance of an iterative over a linear approach. The approach allowed for a multitude of “what if” scenarios to be analysed, leading to more informed sustainable solutions at the right stages of the project development, with a generally lower level of detail (LOD) and computational/modelling effort required. In addition, the final stage of Building Regulations Part L compliance calculations was reached with a lot greater level of certainty, in terms of its requirements. Finally, a strategy for long term performance monitoring and evaluation of the building design in terms of its environmental sustainability is presented, via integration between BIM and SBE (Smart Built Environment) technologies

Integrated modelling framework for the analysis of demand side management strategies in urban energy systems

Influenced by environmental concerns and rapid urbanisation, cities are changing the way they historically have produced, distributed and consumed energy. In the next decades, cities will have to increasingly adapt their energy infrastructure if new low carbon and smart technologies are to be effectively integrated. In this context, advanced planning tools can become crucial to successfully design these future urban energy systems. However, it is not only important to analyse how urban energy infrastructure will look like in the future, but also how they will be operated. Advanced energy management strategies can increase the operational efficiency, therefore reducing energy consumption, CO2 emissions, operational costs and network investments. However, the design and analysis of these energy management strategies are difficult to perform at an urban scale considering the spatial and temporal resolution and the diversity in users energy requirements. This thesis proposes a novel integrated modelling framework to analyse flexible transport and heating energy demand and assess different demand-side management strategies in urban energy systems. With a combination of agent-based simulation and multi-objective optimisation models, this framework is tested using two case studies. The first one focuses on transport electrification and the integration of electric vehicles through smart charging strategies in an urban area in London, UK. The results of this analysis show that final consumer costs and carbon emissions reductions (compared to a base case) are in the range of 4.3-45.0% and 2.8-3.9% respectively in a daily basis, depending on the type of tariff and electricity generation mix considered. These reductions consider a control strategy where the peak demand is constrained so the capacity of the system is not affected. In the second case study, focused on heat electrification, the coordination of a group of heat pumps is analysed, using different scheduling strategies. In this case, final consumer costs and carbon emissions can be reduced in the range of 4-41% and 0.02-0.7% respectively on a daily basis. In this case, peak demand can be reduced in the range of 51-62% with respect to the baseline. These case studies highlight the importance of the spatial and temporal characterisation of the energy demand, and the level of flexibility users can provide to the system when considering a heterogeneous set of users with different technologies, energy requirements and behaviours. In both studies, trade-offs between the environmental and economic performance of demand-side management strategies are assessed using a multi-objective optimisation approach. Finally, further applications of the integrated modelling framework are described to highlight its potential as a decision-making support tool in sustainable and smart urban energy systems.Open Acces

Demand response model development for smart households using time of use tariffs and optimal control - the Isle of Wight energy autonomous community case study

Residential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable-electricity. In this article, a two-stage optimization method is used to implement a price-based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart-meters and a local DR-Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR-scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas-network. Using a distribution network model along with a load flow software-tool, the secondary voltages and apparent-power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large-scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households

Energy system optimisation and smart technologies - a social sciences and humanities annotated bibliography

The challenge: * Systems perspectives on energy involve a holistic view on balancing demand and supply; system optimisation can support security of supply, affordability, sustainability and profitability. * A central, and relatively recent, element of system optimisation is the move towards smart grids, and smart technologies, which concern interconnection of system elements usually through the internet. As well as increasing the resilience of the network, it is hoped this will help “citizens take ownership of the energy transition [and] benefit from new technologies”. * ‘Smartification’ of the energy system introduces a range of new societal conditions and consequences. The aim: * European energy policy has so far mainly relied on research from Science, Technology Engineering and Mathematics (STEM) disciplines. Energy-related Social Sciences and Humanities (energy-SSH) have been significantly underrepresented. The aim of this bibliography is to give policymakers a selected yet broad impression of the SSH research community focusing on ‘energy system optimisation and smart technologies’. Wherever possible, policy deductions or research and innovation recommendations are mentioned. Coverage: * Disciplines covered in this bibliography are broadly representative of the current SSH research community in the area, with a slight bias towards Economics, Sociology and Science & Technology Studies. Nevertheless, robust accounts from Psychology, Politics, Ethnography, Development, Environmental Social Science, Geography, Planning, Law, History and other fields are also included. * Geographically, research presented is primarily from Western and Northern Europe, but with diversity across these regions, and inclusion of some Eastern European and non-European contributions. * Techno-economic accounts are very highly represented in the field of energy system optimisation and smart technologies, a fact highlighted by researchers themselves. Much of this research concentrates on financial cost/benefit of smart grid and technical design, while approaches focusing on social practices or user-centric design are increasing but still underrepresented. The latter were deliberately given higher visibility in this bibliography. Key findings: * Numerous papers presented here focus on how questions of smart technology diffusion, innovation, and adoption might be shifted away from monetary incentives or cost/benefit analyses of technologies. * A unifying message across many topics and disciplines - from energy justice or socio-technical scenarios, to Economics or Ethnography - is that co-operation between techno-economic and SSH approaches needs more attention and is crucial for successful smart grid realisation. * Another important debate for SSH researchers is the deconstruction of overly optimistic visions of smart societies. Many authors urge caution in considering the (financial and social) costs and benefits of smart technologies for all of society, including issues of privacy intrusion. There are calls for more research on both policy initiatives, preferably targeting the community level, and clear communication strategies which fully consider these aspects

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

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