Supporting provenance of digital calibration certificates with temporal databases

Trust in current and historical calibration data is crucial. The recently proposed XML schema for digital calibration certificates (DCCs) provides machine-readability and a common exchange format to enhance this trust. We present a prototype web application developed in the programming language Links for storing and displaying a DCC using a relational database. In particular, we leverage the temporal database features that Links provides to capture different versions of a certificate and inspect differences between versions. The prototype is the starting point for developing software to support DCCs and the data with which they are populated and has underlined that DCCs are the tip of the iceberg in automating the management of digital calibration data, activity that includes data provenance and tracking of modifications

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Random forests and artificial neural network for predicting daylight illuminance and energy consumption

Predicting energy consumption and daylight illuminance plays an important part in building lighting control strategies. The use of simplified or data-driven methods is often preferred where a fast response is needed e.g. as a performance evaluation engine for advanced real-time control and optimization applications. In this paper we developed and then compared the performance of the widely-used Artificial Neural Network (ANN) with Random Forest (RF), a recently developed ensemble-based algorithm. The target application was predicting the hourly energy consumption and daylight illuminance values of a classroom in Cardiff, UK. Overall, RF performed better than ANN for predicting daylight illuminance; with coefficients of determination (R^2) of 0.9881 and 0.9799 respectively. On the energy consumption testing dataset, ANN performed marginally better than RF with R^2 values of 0.9973 and 0.9966 respectively. RF performs internal cross-validation and is relatively easy to tune as it has few tuning parameters. The paper also highlighted possible future research directions

Optimising the scheduled operation of window blinds to enhance occupant comfort

Artificial lighting can increase a energy consumption. On the other hand, the availability of daylight in occupied spaces can reduce energy consumption while positively contributing to occupant wellbeing. However, daylight entering the space through windows needs to be reconciled with heat loss during winter and heat gain during summer, which may affect thermal comfort. In this research, a genetic algorithm is used to optimize the operation schedules of window blinds in a school classroom to enhance occupant visual comfort level. The objective of the optimization study was to reduce the energy consumption while maintaining the daylighting illuminance within the range of 100 lux to 2000 lux. EnergyPlus simulation software was employed as the daylighting and thermal performance calculation engine. The findings evidenced that the proposed genetic algorithm based schedule optimization reduced the HVAC and lighting energy consumption while giving preference to The results showed that the performance of the discussed method could also depend on different seasons. The genetic algorithm reduced the negative impact of solar gains on energy consumption in summer by closi

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

Variational inference of fractional Brownian motion with linear computational complexity

We introduce a simulation-based, amortised Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk. In the second step an invertible neural network generates the posterior distribution of the parameters from the learnt summary statistics using variational inference. We apply our method to infer the parameters of the fractional Brownian motion model from single trajectories. The computational complexity of the amortized inference procedure scales linearly with trajectory length, and its precision scales similarly to the Cram{\'e}r-Rao bound over a wide range of lengths. The approach is robust to positional noise, and generalizes well to trajectories longer than those seen during training. Finally, we adapt this scheme to show that a finite decorrelation time in the environment can furthermore be inferred from individual trajectories

Optimal scheduling strategy for enhancing IAQ, visual and thermal comfort using a genetic algorithm

Buildings account for 40% of total global energy use and contribute towards 30% of total CO2 emissions. Heating ventilation and air conditioning (HVAC) systems are the major sources of energy consumption in buildings, and there has been extensive research focusing on efficiently control them. However, in most cases, this is achieved at the cost of sacrificing thermal, visual and/or IAQ comfort. High level of carbon dioxide – which is commonly used a metric for measuring air quality, can affect student’s ability to concentrate on academic tasks. This research is aimed at developing a method for optimizing the operation of the window opening to facilitate natural ventilation, window blinds to reduce energy consumption and heat recovery unit (HRU) to provide thermally comfortable environment in a low energy educational building (rated BREEAM excellent ≈ LEED platinum). The research employs model-based optimization using a daylight-coupled thermal model in EnergyPlus to model the interrelationships between blind positions; window opening; lighting and heating/cooling energy consumption; and thermal comfort. A simple genetic algorithm has been used to minimize energy consumption, enhance IAQ, thermal and visual comfort (which are competitive constraints with each other). The optimization takes into account the scheduling of the window opening, HRU and blind’ operation in case study building. Optimization results highlighted the potential cost savings while considering energy consumption as an objective function and, thermal comfort, IAQ and visual comfort as constraints. The paper also reports on the directions for further research, in particular on the use of surrogate models and machine learning techniques so that the proposed optimization methods can be used without significant computation overheads

The UDSA ontology: An ontology to support real time urban sustainability assessment

Urban sustainability assessment frameworks have emerged during the past decade to address holistically the complexity of the urban landscape through a systems approach, factoring in environmental, social and economic requirements. However, the current assessment schemes are (a) static in nature, and as such don't reflect the dynamic and real-time nature of urban artefacts, (b) are not grounded in semantics (e.g. BIM and GIS), and (c) are at best used to assist in regulatory compliance, for instance in energy design, to meet increasingly stringent regulatory requirements. Information and communication technologies provide a new value proposition capitalizing on the Internet of Things (IoT) and semantics to provide real-time insights and inform decision making. Consequently, there is a real need in the field for data models that could facilitate data exchange and handle data heterogeneity. In this study, a semantic data model is considered to support near real-time urban sustainability assessment and enhance the semantics of sensor network data. Based on an extensive review of urban sustainability assessment frameworks and ontology development methodologies, the Urban District Sustainability Assessment (UDSA) ontology has been developed and validated using real data from the site of “The Works”, a newly refurbished neighbourhood in Ebbw Vale, Wales. This novel approach reconciles several domain-specific ontologies within one high-level ontology that can support the creation of real-time urban sustainability assessment software. In addition, this information model is aligned with 29 authoritative urban sustainability assessment frameworks, thus providing a useful resource not only in urban sustainability assessment, but also in the wider smart cities context

Towards the adoption of automated regulatory compliance checking in the built environment

Automated compliance checking brings advantages to the built environment but, currently, there has been no meaningful adoption, despite the increasing maturity of asset information models. This paper addresses this by ascertaining the blockers/obstacles to adoption and develops a road-map to overcome them. This work has been conducted in the UK and a road-map has been produced to drive forward adoption. More specifically this paper has assessed the current state of the art in the field and engaged with industry to examine the attitudes to the digitisation of regulatory compliance processes. The results showed that industry believes that adoption of automation was both feasible and desirable, with the caveat that human oversight be maintained. Our road-map’s methodical list of steps was judged to have the potential to bring the construction industry to the verge of mass industrialisation of auto mated compliance checking by 2025