Existing Building Management System Research Articles

Demonstrating a smart controller in a hospital integrated energy system

Integrated energy systems have recently gained primary importance in clean energy transition. The combination of the electricity, heating and gas sectors can improve the overall system efficiency and integration of renewables by exploiting the synergies among the energy vectors. In particular, real-time optimization tools based on Model Predictive Control (MPC) can considerably improve the performance of systems with several conversion units and distribution networks by automatically coordinating all interacting technologies. Despite the relevance of several simulation studies on the topic, however, it is significantly harder to have an experimental demonstration of this improvement. This work presents a methodology for the real-world implementation of a novel smart control strategy for integrated energy systems, based on two coordinated MPC levels, which optimize the operation of all conversion units and all energy vectors in the short- and long-term, respectively, to account also for economic incentives on critical units. The strategy that was previously developed and evaluated in a simulation environment has now been implemented, as a supervisory controller, in the integrated energy system of a hospital in Italy. The optimal control logic is easily actuated by dynamically communicating the optimal set-points to the existing Building Management System, without having to alter the system configuration. Field data collected over a two-year period, firstly when it was business as usual and when the new operation was introduced, show that the MPC increased the economic margin and revenues from yearly incentives and lowered the amount of electricity purchased, reducing dependency on the power grid.

Design and Performance Investigation of a Pilot Micro-Grid in the Mediterranean: MCAST Case Study

This paper discusses the simulation framework developed for an in-campus pilot micro-grid at MCAST, Malta, to enhance its efficiency and reliability. One year of real-time metered data were used to arrive at the load curves, categorize the loads as essential and non-essential ones, and decide the micro-grid domain within MCAST. The potential scenarios were modeled to observe the behavior of the present status of the micro-grid, with an increased photovoltaic (PV) generation capacity, by using an optimum battery storage system with a diesel generator of suitable capacity and finally integrating electric vehicles (EVs) to discuss the potential of vehicle to grid (V2G) operation modes. The existing building management system (BMS) of MCAST was interfaced within the micro-grid to introduce the geographic information system (GIS) and Building Information Modeling (BIM) for developing an intelligent 3D model of the micro-grid. The results of the simulation framework for various potential case scenarios were obtained in a MATLAB/Simulink environment to assess the performance of the micro-grid. Previously formulated key performance indices (KPIs) that describe the financial aspects of micro-grid operation and ecological benefits of the investigated micro-grid were evaluated. A sensitivity analysis of these KPIs shows encouraging results with the potential of cost-competitiveness.

Demystifying Thermal Comfort in Smart Buildings: An Interpretable Machine Learning Approach

Thermal comfort is a key consideration in smart buildings and a number of comfort models are available nowadays to evaluate the comfort level of occupants. However, the models are often complex and hardly interpretable for the developers and operators. Indeed, the model interpretations are beneficial in multifold such as for system inspection and optimization. In this article, we propose an interpretable thermal comfort system to introduce interpretability to any black-box comfort models. First, we focus on the relationship between a model's input features and output comfort level. The feature impact on comfort is investigated and the impact patterns are shown to be diverse for different features. Second, we unveil the model mechanisms about the data processing inside the model by building the model surrogates based on the interpretable machine learning algorithms. The surrogates offer outstanding fidelity for simulating the actual model mechanisms and the interpretations based on the surrogates are intuitive and informative. Our interpretable comfort system can be integrated with the existing building management systems. Accordingly, we can ease building owner's concerns about adopting new black-box technologies and enable various smart building applications like smart energy management.

Neuron – Digital console innovative by Arup

At the age of digital era, supervised / unsupervised machine learning, sharp data analysis and neuron network are some of many ways to formulate optimization problems with millions or billions of variables. Classical building system and environmental optimization algorithms are not designed to scale to instances of this size; new approaches are needed.The Digital Platform developed by Arup – Neuron is an innovative console which embodies 5G, IoT, Big Data, Cloud Computing and AI technology for smart building with advanced data analytic capabilities. It can fully integrate with the existing Building Management System as well as various data sources generated from the IoT sensors, enabling prompt and adaptive response to dynamic environment, serving as a foundation for buildings to consolidate and connect data from disparate equipment and devices to provide customised insight and machine learning models for built environmental management and improvement, new building design, operation and asset management, energy monitoring and optimisation.One significant success achieved with our integrated digital platform is that it has been adopted by multiple clients in the East Asia region in managing their building. With full implementation and other appropriate energy saving and/or retro-commissioning practices, Neuron is expected to save up to 30% total HVAC energy consumption for a typical existing commercial building. Apart from energy saving, Neuron also focuses on creating a better indoor environment, which could significantly improve employees’ productivity.Neuron, as a smart building console, is set to have a momentous impact on Hong Kong, not only for a better built environment, but also a game changer for the East Asia region to excel in the era of digital economy. This paper elaborates how Neuron could help in digital transformation of new and existing buildings in the new era. The challenges and experiences of projects will also be discussed.Start your abstract here…

Temperature and Load Consumption Forecast in Smart Building on Foundation IoT by ARIMA Algorithm

The paper presents the application of Internet of thing (IoT) in managing smart buildings and a proposal to study some of the functions, applications of building management system (BMS) in monitoring, controlling and using electricity effectively for high-rise buildings. Currently, high-rise buildings consume about 33% of global electricity. Managing energy consumption in the buildings is very important when the demand for electricity is increasing. Existing building management systems have high costs and reveal many weaknesses in data collection. Therefore, using the ARIMAX algorithm for predicts temperature, humidity and the amount of electricity that will be consumed in building which helps operators always plan to prepare the necessary energy source for the building, ensuring the electric energy is always provided fully, continuously and effectively

Sustainability in university campus: options for achieving nearly zero energy goals

PurposeThe purpose of this study was to design a renovation plan for a university campus building (Department of Electrical and Computer Engineering) with the aim to achieve nearly zero energy performance, ensuring a low specific demand (lower than 44 kWh/m2) and a high level of on-site renewable generation (equivalent to more than 20 per cent of the energy demand).Design/methodology/approachThe baseline demand was characterized based on energy audits, on smart metering data and on the existing building management system data, showing a recent reduction of the electricity demand owing to some implemented measures. The renovation plan was then designed with two main measures, the total replacement of the actual lighting by LEDs and the installation of a photovoltaic system (PV) with 78.8 kWp coupled with an energy storage system with 100 kWh of lithium-ion batteries.FindingsThe designed renovation achieved energy savings of 20 per cent, with 27.5 per cent of the consumed energy supplied by the PV system. This will ensure a reduction of the specific energy of the building to only 30 kWh/m2, with 42.4 per cent savings on the net-energy demand.Practical implicationsThe designed renovation proves that it is possible to achieve nearly zero energy goals with cost-effective solutions, presenting the lighting renovation and the solar PV generation system a payback of 2.3 and 6.9 years, respectively.Originality/valueThis study innovated by defining ambitious goals to achieve nearly zero energy levels and presenting a design based on a comprehensive lighting retrofit and PV generation, whereas other studies are mostly based on envelope refurbishment and behaviour changes.

Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study

A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized1The terms “decentralized”, “distributed” and “agent-based” are considered to have similar meanings herein.1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based, model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

Demand response optimized heat pump control for service sector buildings

With an increasing amount of volatile renewable electrical energy, the balancing of demand and supply becomes more and more demanding. Demand response is one of the emerging tools in this new landscape. Targeting service sector buildings, we investigated a tariff driven demand response model as a means to shave electrical peak loads and thus reducing grid balancing energy. In this paper is presented a software framework for load shifting which uses a tariff signal for the electric energy as minimization target. The framework can be used both on top of an existing building management system to shift heat generation towards low-tariff times, as well as to simulate load shifting for different buildings, heat pumps and storage configurations. Its modular architecture allows us to easily replace optimizers, weather data providers or building management system adapters. Our results show that even with the current TOU tariff system, up to 34 % of cost savings and up to 20 % reduction in energy consumption can be achieved. With Sub-MPC, a modified MPC optimizer, we could reduce computing times by a factor 50, while only slightly affecting the quality of the optimization.

Towards green building performance evaluation using asset information modelling

Purpose– The purpose of this paper is to provide a unique conceptual framework for integrated asset management strategy that includes making use of available facility assessment methods and tools such as BREEAM In-Use, and Leadership in Energy and Environmental Design (LEED); and highlights proposes areas of commonality between these and the use of as-built Building Information Modelling, that ultimately becomes the Asset Information Model (AIM). This framework will consider the emerging requirements for the capture of Building Performance Attribute Data (BPAD), and how these can be managed in order to assist with effective post-construction building performance evaluation.Design/methodology/approach– A review of the current process relevant to the development of as-built BIMs and AIMs was undertaken which included a discussion of BIM standards and of the COBie process. This was combined with data provided by industry practitioners. This led to the concept of BPADs being developed, to be used within existing green building tool, BREEAM In-Use, COBIE and FM/Asset management methods. In turn these methodologies were used to identify possible synergies and areas of integration in AIM-enabled environments.Findings– Recognising the cyclical nature of asset management and BIM, a conceptual model was generated. It was found that BPADs could be aggregated within an AIM model which could influence the delivery of effective facilities and asset management. The model considers the use of existing Building Management Systems (BMS) and Computer Aided Facility Management Systems (CAFMs) and identifies issues associated with the overall sustainability strategy.Originality/value– A conceptual framework is generated that proposes the use of effective information management and aggregation of BPAD within an AIM.

Design and deployment of a new wireless sensor node platform for building environmental monitoring and control

It is commonly agreed that a 15–40% reduction of building energy consumption is achievable by efficiently operated buildings when compared with typical practice. Existing research has identified that the level of information available to Building Managers with existing Building Management Systems and Environmental Monitoring Systems is insufficient to perform the required performance-based building assessment. The majority of today’s buildings are insufficiently sensored to obtain an unambiguous understanding of performance. The cost of installing additional sensors and meters is extremely high, primarily due to the estimated cost of wiring and the needed labour. From these perspectives wireless sensors technology proves to have a greater cost-efficiency while maintaining high levels of functionality and reliability. In this paper, a wireless sensor network mote hardware design and implementation are introduced particularly for building deployment application. The core of the mote design is based on the 8-bit AVR microcontroller, Atmega1281 and 2.4 GHz wireless communication chip, CC2420. The sensors were selected carefully to meet both the building monitoring and design requirements. Beside the sensing capability, actuation and interfacing to external meters/sensors are provided to perform different management control and data recording tasks.

Evaluating performance indices of a shopping centre and implementing HVAC control principles to minimize energy usage

Heating, ventilating and air-conditioning (HVAC) systems in buildings must be integrated with an efficient control scheme to maintain comfort under any load conditions. Efficient HVAC control is often the most cost-effective option to improve the energy efficiency of a building. However, HVAC processes are non-linear, and characteristics change on a seasonal basis so the effect of changing the control strategy is usually difficult to predict. The present study aims to reduce energy consumption by defining new HVAC control strategies and tuning control loops in a shopping centre. First, an energy audit was performed to investigate the potential for energy savings and to redefine the control scenarios, while a methodology for the shopping centre was developed. Performance indices were then calculated and compared with the yardsticks. Next, normalised performance indices were computed to reach out a better understanding of the building’s efficiency. Finally, new strategies were implemented with the help of the existing building management system (BMS) and about 22% of energy saving was achieved.

Modelling the passive thermal response of a building using sparse BMS data

This paper describes the application of system identification to the thermal response of a conventional office space using data collected from an existing building management system. Autoregressive models are estimated and it is found that the output-error model provides the best fit between simulated and measured internal-air temperature data. The inclusion of electricity consumption data improves the fit for all model structures used, though this is evidently not modelled as providing a simple internal heat-gain. The model error is comparable with that reported for models validated in laboratory test rooms, but without the use of full instrumentation.