Modelling and disturbance estimation for model predictive control in building heating systems

O’Dwyer, Edward; Tommasi, Luciano De; Kouramas, Konstantinos; Cychowski, Marcin; Lightbody, Gordon

doi:10.1016/j.enbuild.2016.08.077

Cited by 31 publications

(28 citation statements)

References 33 publications

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“…In the literature a number of approaches are reported for the simulation of building/district energy systems and related controls that exploit physics-based and/or reduced-order models. Relevant model parameters need to be properly determined and calibrated to match those of the considered case study [28][29][30][31].…”

Section: Methodology For Refurbishment Scenarios Generation and Modelmentioning

confidence: 99%

“…A general knowledge of the sites to be refurbished is helpful to apply the presented methodology and tool more effectively, given the large number of scenarios that can be generated using the built-in ECM catalogues. Accurate modelling of system components along with their controls, as well as the optimization algorithms, are the key ingredients to be incorporated in a decision support platform for refurbishment design [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. We focus on the simulation procedure and ascertaining the impact HVAC and controls to have pertinent performance indicators.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Comparative Evaluation of Building and District Control-Oriented Energy Retrofit Scenarios

et al. 2018

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This paper presents work undertaken as part of the European H2020 project OptEEmAL (Optimized Energy Efficient Design Platform for Refurbishment at District Level), toward development of a decision-support platform for building and district refurbishment interventions. We describe a methodology for generation and evaluation of refurbishment scenarios for building and districts with particular emphasis on "active" energy conservation measures (i.e., installation or replacement of heating, ventilation, air conditioning (HVAC) systems) and related controls. The impact of HVAC and controls on energy and economic key performance indicators are usually neglected or very simplified in existing energy simulation tools. We apply a model-based approach to evaluate key-performance indicators related to energy consumption and energy costs in buildings and districts, such that possible refurbishment alternatives can be easily evaluated, thereby showing how a smart decision support tool will allow stakeholders to compare multiple alternatives quickly. By considering relevant case studies at building and district level, including refurbishment of heating and cooling plants, we highlight, in a simulation-based study, how the deployment of efficiency-based controls enable significant energy savings thanks to the exploitation of the model-based approach. This way, additional motivations for energy savings and ultimately for new investments in energy-related technologies are provided.

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Section: Methodology For Refurbishment Scenarios Generation and Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Based Comparative Evaluation of Building and District Control-Oriented Energy Retrofit Scenarios

et al. 2018

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“…The prominence of buildings as significant energy consumers in the global energy landscape is well understood, with 40% of global energy consumption typically assigned to the general building sector [39]. Despite including complex heating, cooling and electrical demands, traditional rule-based approaches for managing relevant components and determining operational decisions dominate, leading to unnecessary inefficiencies [40]. In anticipation of the future decarbonisation of the electricity grid, a desire to move towards electricity-based heat sources (primarily through heat pumps) has emerged in recent times [41], furthering the case for integrated energy approaches [42].…”

Section: The Built Environmentmentioning

confidence: 99%

“…While any of the above approaches can be integrated into a smart energy management strategy, success is very much predicated on sufficient data quality. This requires a good degree of excitation and a low-level of unmeasured disturbance -as highlighted in [40]. Hybrid approaches that can blend expert knowledge with empirical data effectively are an attractive prospect.…”

Section: Modelling the Thermal Behaviour Of Buildingsmentioning

confidence: 99%

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

et al. 2019

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Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

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“…Our cities, homes and even ourselves are amassed with technology for monitoring, collating and analyzing data. From a vision of smart cities [Townsend, 2014] in which the very control of home heating is managed through analytical decisions [O'Dwyer et al, 2016] through to effective control of power generation [British Gas], it is clear to see how such analysis opens up the potential for affecting power consumption and ultimately impacts the global fuel crisis.…”

Section: Introductionmentioning

confidence: 99%

Custom Hardware Versus Cloud Computing in Big Data

Lightbody

Browne

Haberland

2017

Advanced Information and Knowledge Processing

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The computational and data handling challenges in big data are immense yet a market is steadily growing traditionally supported by technologies such as Hadoop for management and processing of huge and unstructured datasets. With this ever increasing deluge of data we now need the algorithms, tools and computing infrastructure to handle the extremely computationally intense data analytics, looking for patterns and information pertinent to creating a market edge for a range of applications. Cloud computing has provided opportunities for scalable high-performance solutions without the initial outlay of developing and creating the core infrastructure. One vendor in particular, Amazon Web Services, has been leading this field. However, other solutions exist to take on the computational load of big data analytics. This chapter provides an overview of the extent of applications in which big data analytics is used. Then an overview is given of some of the high-performance computing options that are available, ranging from multiple Central Processing Unit (CPU) setups, Graphical Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and cloud solutions. The chapter concludes by looking at some of the state of the art solutions for deep learning platforms in which custom hardware such as FPGAs and Application Specific Integrated Circuits (ASICs) are used within a cloud platform for key computational bottlenecks.

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Modelling and disturbance estimation for model predictive control in building heating systems

Cited by 31 publications

References 33 publications

Model-Based Comparative Evaluation of Building and District Control-Oriented Energy Retrofit Scenarios

Model-Based Comparative Evaluation of Building and District Control-Oriented Energy Retrofit Scenarios

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

Custom Hardware Versus Cloud Computing in Big Data

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