High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Modeling and model calibration for model predictive occupants comfort control in buildings

Mathematical models are essential in Model-Predictive Control (MPC) for building automation and control (BAC) application, which must be precise and computationally efficient for realtime optimization and control. However, building models are of high complexity because of the nonlinearities of heat and mass transfer processes in buildings and their air-conditioning and mechanical ventilation (ACMV) systems. This paper proposes a method to develop an integrated linear model for indoor air temperature, humidity and Predicted Mean Vote (PMV) index suitable for fast real-time multiple objectives optimization. A linear dynamic model is developed using SIMSCAPE language based on the BCA SkyLab test bed facility in Singapore as a case study. Experimental data is used to calibrate the model using trust-region-reflective least squares optimization method. The results show that the mean absolute percentage errors (MAPE) of predicted room temperature and humidity ratio are 1.25% and 4.98%, compared to measurement, respectively

Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

Towards an Automated Tool Chain for MPC in Multi-zone Buildings

Heating Ventilation and Air Conditioning (HVAC) represents a large fraction of the world’s primary energy demand. Novel control strategies such as Model Predictive Control (MPC) aim to reduce energy use, while also improving occupant comfort. For MPC to be a viable alternative to classical Rule Based Control (RBC), it should be able to incorporate multiple emission and production systems, multiple zones, and aspects such as thermal comfort and indoor air quality (IAQ). Identifying grey-box or black-box MPC controller models for hybrid energy systems in multi-zone buildings has proven to be difficult. White-box models use physical knowledge of the system and take into account the desired dynamics. This approach however requires a substantial time investment since every building requires a custom model. The goal of this paper is to describe on-going work aiming at an automated methodology for setting up MPC controllers for buildings using white-box models. For this methodology, firstly, a detailed ‘emulator’ model of the building needs to be developed. Secondly this emulator is linearised to obtain a state space formulation of the building system and thirdly the emulator is used to generate MPC input data such as disturbances. Fourthly, a custom MPC tool uses this information to compute optimal control set points. These steps are elaborated below. The first step of the methodology is to the IDEAS library in Modelica to set up a detailed building envelope model. Modelica is an object-oriented equation based language that allows assembling complex systems by combining component models from open source libraries such as IDEAS. The second step is to linearise the building model. The IDEAS library is parametrized such that non-linearities such as convection correlations and radiation can be linearised around a well-chosen working point. Hydraulic connections from the HVAC are simplified and converted into heat flow rates. The HVAC is therefore simplified such that their the heat flow rate set points can be optimized. Disturbances such as the ambient temperature are also model inputs. The state space model resulting from the linearisation, although linear, accurately predicts the temperatures of the building’s zones. In the third step the emulator model is used to compute and store time series data for all state space model inputs such as ambient and radiative temperatures, solar incidence on glazing and internal gains from occupants. In step four the highly accurate state space model (step two) and corresponding input data (step three) files are used to set up the MPC problem. This MPC optimizes the remaining inputs from the state space model, subject to constraints and using a cost function that are passed to the optimization problem using augmented rows of the state space matrices. The state space matrices are pre-processed using CasADi such that a computationally efficient linear program is generated. This methodology is demonstrated on a medium size office building with 32 zones and hybrid emission and production systems. Results and performance are discussed. The strong points are the applicability to hybrid energy systems in multi-zone buildings, allowing the evaluation of thermal comfort and IAQ in different zones.

Ontology-based modeling of control logic in building automation systems

The control logic implemented in building automation systems (BAS) has a significant impact on the overall energy demand of the building. However, information on the control logic, if documented, is often concealed from further data integration and reuse in heterogeneous information silos using disparate data formats. In particular, existing data formats and information models offer limited support to describe control logic explicitly. Ontology-based modeling of the control logic of BAS can potentially result in a versatile source of information for information-driven processes to further increase the performance of technical equipment in a building. Therefore, we present a novel information model, CTRLont, which allows to formally specify the domain of control logic in BAS. We demonstrate the usefulness of the novel information model by using it as a knowledge base for automating rule-based verification of designed control logic in BAS. We successfully apply the methodology to a simple control of an air handling unit and indicate a number of future steps

Design of knowledge-based systems for automated deployment of building management services

Despite its high potential, the building's sector lags behind in reducing its energy demand. Tremendous savings can be achieved by deploying building management services during operation, however, the manual deployment of these services needs to be undertaken by experts and it is a tedious, time and cost consuming task. It requires detailed expert knowledge to match the diverse requirements of services with the present constellation of envelope, equipment and automation system in a target building. To enable the widespread deployment of these services, this knowledge-intensive task needs to be automated. Knowledge-based methods solve this task, however, their widespread adoption is hampered and solutions proposed in the past do not stick to basic principles of state of the art knowledge engineering methods. To fill this gap we present a novel methodological approach for the design of knowledge-based systems for the automated deployment of building management services. The approach covers the essential steps and best practices: (1) representation of terminological knowledge of a building and its systems based on well-established knowledge engineering methods; (2) representation and capturing of assertional knowledge on a real building portfolio based on open standards; and (3) use of the acquired knowledge for the automated deployment of building management services to increase the energy efficiency of buildings during operation. We validate the methodological approach by deploying it in a real-world large-scale European pilot on a diverse portfolio of buildings and a novel set of building management services. In addition, a novel ontology, which reuses and extends existing ontologies is presented.The authors would like to gratefully acknowledge the generous funding provided by the European Union’s Horizon 2020 research and innovation programme through the MOEEBIUS project under grant agreement No. 680517

Construction industry 4.0 and sustainability: an enabling framework

Governments worldwide are taking actions to address the construction sector's sustainability concerns, including high carbon emissions, health and safety risks, low productivity, and increasing costs. Applying Industry 4.0 technologies to construction (also referred to as Construction 4.0) could address some of these concerns. However, current understanding about this is quite limited, with previous work being largely fragmented and limited both in terms of technologies as well as their interrelationships with the triple bottom line of sustainability perspectives. The focus of this article is therefore on addressing these gaps by proposing a comprehensive multi-dimensional Construction 4.0 sustainability framework that identifies and categorizes the key Construction 4.0 technologies and their positive and negative impacts on environmental, economic, and social sustainability, and then establishing its applicability/usefulness through an empirical, multimethodology case study assessment of the UAE's construction sector. The findings indicate Construction 4.0’s positive impacts on environmental and economic sustainability that far outweigh its negative effects, although these impacts are comparable with regards to social sustainability. On Construction 4.0 technologies itself, their application was found to be nonuniform with greater application seen for building information modeling and automation vis-à-vis others such as cyber-physical systems and smart materials, with significant growth expected in the future for blockchain- and three-dimensional-printing-related technologies. The proposed novel framework could enable the development of policy interventions and support mechanisms to increase Construction 4.0 deployment while addressing its negative sustainability-related impacts. The framework also has the potential to be adapted and applied to other country and sectoral contexts