Real-time Multi-scale Smart Energy Management and Optimisation (REMO) for buildings and their district

Energy management systems in buildings and their district today use automation systems and artificial intelligence (AI) solutions for smart energy management, but they fail to achieve the desired results due to the lack of holistic and optimised decision-making. A reason for this is the silo-oriented approach to the decision-making failing to consider cross-domain data. Ontologies, as a new way of processing domain knowledge, have been increasingly applied to different domains using formal and explicit knowledge representation to conduct smart decision-making. In this PhD research, Real-time Multiscale Smart Energy Management and Optimisation (REMO) ontology was developed, as a cross-domain knowledge-base, which consequently can be used to support holistic real-time energy management in districts considering both demand and supply side optimisation. The ontology here, is also presented as the core of a proposed framework which facilitates the running of AI solutions and automation systems, aiming to minimise energy use, emissions, and costs, while maintaining comfort for users. The state of the art AI solutions for prediction and optimisation were concluded through authors involvement in European Union research projects. The AI techniques were independently validated through action research and achieved about 30 - 40 % reduction in energy demand of the buildings, and 36% reduction in carbon emissions through optimisation of the generation mix in the district. The research here also concludes a smart way to capture the generic knowledge behind AI models in ontologies through rule axiom features, which also meant this knowledge can be used to replicate these AI models in future sites. Both semantic and syntactic validation were performed on the ontology before demonstrating how the ontology supports the various use cases of the framework for holistic energy management. Further development of the framework is recommended for the future which is needed for it to facilitate real-time energy management and optimisation in buildings and their district

Design of a Calorimetric Test Facility to Replicate Real Boundary Conditions in the Gulf Countries

The design and modelling of a calorimetric test infrastructure for building envelopes is performed for the side-by-side assessment of different building envelope systems. The infrastructure is designed for representing transient weather conditions in Middle east. It consists of 3 “cold” experimental chambers and a larger “hot” experimental chamber. All three cold chambers have one equally sized envelope element exposed to the larger chamber. The test facility is designed to allow testing on walls and roofs, where different envelope insulation systems will be installed over a common substrate. Heating and cooling loads of all experimental chambers are calculated, and systematic load differences assessed. Heat flow across test samples and other surfaces in the test are calculated. Insulation levels of envelope surfaces in experimental chambers are specified to provide a good match between heat transfer across test samples and heat input to experimental chambers

Towards the next generation of smart grids: semantic and holonic multi-agent management of distributed energy resources

The energy landscape is experiencing accelerating change; centralized energy systems are being decarbonized, and transitioning towards distributed energy systems, facilitated by advances in power system management and information and communication technologies. This paper elaborates on these generations of energy systems by critically reviewing relevant authoritative literature. This includes a discussion of modern concepts such as ‘smart grid’, ‘microgrid’, ‘virtual power plant’ and ‘multi-energy system’, and the relationships between them, as well as the trends towards distributed intelligence and interoperability. Each of these emerging urban energy concepts holds merit when applied within a centralized grid paradigm, but very little research applies these approaches within the emerging energy landscape typified by a high penetration of distributed energy resources, prosumers (consumers and producers), interoperability, and big data. Given the ongoing boom in these fields, this will lead to new challenges and opportunities as the status-quo of energy systems changes dramatically. We argue that a new generation of holonic energy systems is required to orchestrate the interplay between these dense, diverse and distributed energy components. The paper therefore contributes a description of holonic energy systems and the implicit research required towards sustainability and resilience in the imminent energy landscape. This promotes the systemic features of autonomy, belonging, connectivity, diversity and emergence, and balances global and local system objectives, through adaptive control topologies and demand responsive energy management. Future research avenues are identified to support this transition regarding interoperability, secure distributed control and a system of systems approach

Utilizing artificial neural network to predict energy consumption and thermal comfort level: an indoor swimming pool case study

This paper presents an Artificial Neural Network (ANN) approach to predict energy consumption and thermal comfort level (represented by Predicted Mean Vote (PMV)) of an indoor swimming pool. In a swimming pool, several environmental and control variables, directly and/or indirectly, affect energy consumption and thermal comfort, rendering difficult the development of a mathematical relationship amongst input and output variables. Thus, an ANN based prediction approach is used to elicit this relationship within a reasonable period of time. This forms the basis of an optimization based control system to evaluate the control parameters in the swimming pool. The proposed approach is implemented for a specific Heating, Ventilation, and Air Conditioning (HVAC) system, based on use cases/scenarios developed in close consultation with site engineers and domain experts. Due to lack of meaningful historical monitored data (from sensors and smart meters), a calibrated simulation model is used to generate large amount of data sets to train the corresponding ANN prediction engine. The trained ANN was then calibrated in real conditions and used as a cost function in an optimization program to help achieve energy saving targets. Several ANN algorithms have been tested and benchmarked leading to the selection, with further tuning, of the best performing ANN algorithm, namely Levenberg-Marquardt training algorithm. The latter was used and achieved good results as demonstrated in the selected case study

A HPC based cloud model for real-time energy optimisation

Recent research has emphasised that an increasing number of enterprises need computation environments for executing HPC (High Performance Computing) applications. Rather than paying the cost of ownership and possess physical, fixed capacity clusters, enterprises can reserve or rent resources for undertaking the required tasks. With the emergence of new computation paradigms such as cloud computing it has become possible to solve a wider range of problems due to their capability to handle and process massive amounts of data. On the other hand, given the pressing regulatory requirement to reduce the carbon footprint of our built environment, significant researching efforts have been recently directed towards simulation-based building energy optimisation with the overall objective of reducing energy consumption. Energy optimisation in buildings represents a class of problems that requires significant computation resources and generally is a time consuming process especially when undertaken with building simulation software, such as EnergyPlus. In this paper we present how a HPC based cloud model can be efficiently used for running and deploying EnergyPlus simulation-based optimisation in order to fulfil a number of objectives related to energy consumption. We describe and evaluate the establishment of such an application-based environment, and consider a cost perspective to determine the efficiency over several cases we explore. This study identifies the following contributions: (i) a comprehensive examination of issues relevant to the HPC community, including performance, cost, user perspectives and range of user activities, (ii) a comparison of two different execution environments such as HTCondor and CometCloud and determine their effectiveness in supporting simulation-based optimisation and (iii) a detailed performance analysis to locate the limiting factors of these execution environments

An analytical optimization model for holistic multiobjective district energy management - a case study approach

Efficient management during the operational phase of district energy systems has become increasingly complex due to the various static and dynamic factors involved. Existing deterministic algorithms which are largely based on human experience acquired from specific domains, normally fail to consider the overall efficiency of district energy systems in a holistic way. This paper looks into taking a black box approach by using genetic algorithms (GA) to solve a multiobjectiveoptimization problem conforming to economic, environmental and efficiency standards. This holistic optimization model, takes into account both heat and electricity demand profiles, and was applied in Ebbw Vale district, in Wales. The model helps compute optimized daily schedules for the generation mix in the district and different operational strategies are analyzed using deterministic and genetic algorithm (GA) based combined optimization methods. The results evidence that GA can be used to define an optimum strategy behind heat production leading to an increase in profit by 32% and reduction in CO2 emissions by 36% in the 24 hour period analyzed. This research fits in well with future district energy systems which give priority to integrated and systematic management

A modular optimisation model for reducing energy consumption in large scale building facilities

With the pressing regulatory requirement to increase energy efficiency in our built environment, significant researching efforts have been recently directed towards energy optimisation with the overall objective of reducing energy consumption. Energy simulation and optimisation identify a class of applications that demand high performance processing power in order to be realised within a feasible time-frame. The problem becomes increasingly complex when undertaking such energy simulation and optimisation in large scale buildings such as sport facilities where the generation of optimal set points can be timing inefficient. In this paper we present how a modular based optimisation system can be efficiently used for running energy simulation and optimisation in order to fulfil a number of energy related objectives. The solution can address the variability in building dynamics and provide support for building managers in implementing energy efficient optimisation plans. We present the optimisation system that has been implemented based on energy saving specifications from EU FP7 project – SportE2 (Energy Efficiency for Sport Facilities) and evaluate the efficiency of the system over a number of relevant use-case scenarios

Web-based 3D urban decision support through intelligent and interoperable services

The application of information and communications technology to support urban operational decision makers has received vast interest from industry and academia. This has helped to mature several fields of research within the smart city domain, such as the internet of things, cybernetics, and informatics. However, these fields of research remain siloed, which leads to a clear gap in the literature. The paper recognizes the mentioned gap manifesting in a new smart urban area in Wales, UK, and presents a platform which intends to demonstrate the benefits of exploiting the synergies between these fields of research. Following consultation with various stakeholders at the pilot site, the platform utilizes advanced sensing, analytics, interoperability, and visualization components to provide valuable human-machine interactions to facility managers in the district. Delivering this high value knowledge in a timely, engaging, and accessible manner through advanced decision support interfaces. The paper presents the platform's software architecture, before discussing the decision support interface, intelligent web services, and interoperability components in more detail. The solution's key contributions beyond existing internet of things platforms are the use of a 3D game engine, machine learning and optimization web services, and the integration across the knowledge value chain. This knowledge integration is achieved through semantic modelling of the buildings, urban environment, socio-technical systems, and smart devices in the district

High throughput computing based distributed genetic algorithm for building energy consumption optimization

Simulation based energy consumption optimization problems of complicated building, solved by stochastic algorithms, are generally time-consuming. This paper presents a web-based parallel GA optimization framework based on high-throughput distributed computation environment to reduce the computation time of complex building energy optimization applications. The optimization framework has been utilized in an EU FP7 project - SportE2 (Energy Efficiency for Sport Facilities) to conduct large scale buildings energy consumption optimizations. The optimization results achieved for a testing building, KUBIK in Spain, showed a significant computation time deduction while still acquired acceptable optimal results