Stochastic Model Predictive Control of Mixed-mode Buildings Based on Probabilistic Interactions of Occupants With Window Blinds

Between 4% to 20% of energy used for HVAC, lighting and refrigeration in a building is wasted due to issues associated with systems operations. It is estimated that proper building energy load control and operation can result in up to 40% utility cost savings. Current heuristic rules based on decision trees are difficult to define, manage and optimize as buildings become more complex. Advanced control strategies with weather forecast and cooling load anticipation, known as model predictive control (MPC), offer an attractive alternative for buildings with slow dynamics. However, MPC is mostly practiced through deterministic approaches. Deterministic MPC implicitly assumes that a dynamic model is able to perfectly predict the future behavior of the building over the desired control window, or prediction horizon. However, this assumption is clearly not rational because there will be both modeling errors and disturbances acting on the system over this period. One of these disturbances is associated with building occupant behaviors which interfere with deterministic assumptions. In this study, a probabilistic model of occupants’ behavior on window blind closing event is used to represent the disturbance coming from interactions of building residents with window blinds. This model is a multiple logistic regression analysis, based on a field study in an office building at the University of California, Berkeley (Inkarojrit, 2005). It considers the incident solar radiation on window surface and occupants’ self-reported brightness sensitivity as variable parameters to predict the closing event of blinds with 86.3% of accuracy. The probability of closing event is compared with a random number from the uniform distribution on the interval [0,1] at each time step and if it is greater than the random number, some indicator function will be equal to 1 (closing action) and vice versa. In order to implement the stochastic MPC, Monte Carlo simulation needs to be conducted due to the randomness of occupants’ behavior in closing the blinds. A test-building with mixed-mode cooling and high solar gains is considered as a test-bed. In our methodology, a detailed dynamic building model is developed and it is then used to identify the parameters of a 4th order linear time-variant state-space model. In the MPC formulation, the window opening schedule is optimized for the upcoming prediction horizon and the cost function is the minimization of energy usage subject to thermal comfort constraints during this horizon. Optimal control sequences based on the proposed stochastic MPC framework will be compared with deterministic MPC approaches to investigate possible advantages of considering uncertainties of occupant actions in model predictive controllers of buildings. References: Inkarojrit V., 2005. Balancing Comfort: Occupants’ Control of Window Blinds in Private Offices. PhD thesis, School of Architecture, University of California Berkeley

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

An Economic Model-Based Predictive Control to Manage the Users' Thermal Comfort in a Building

The goal of maintaining users' thermal comfort conditions in indoor environments may require complex regulation procedures and a proper energy management. This problem is being widely analyzed, since it has a direct effect on users' productivity. This paper presents an economic model-based predictive control (MPC) whose main strength is the use of the day-ahead price (DAP) in order to predict the energy consumption associated with the heating, ventilation and air conditioning (HVAC). In this way, the control system is able to maintain a high thermal comfort level by optimizing the use of the HVAC system and to reduce, at the same time, the energy consumption associated with it, as much as possible. Later, the performance of the proposed control system is tested through simulations with a non-linear model of a bioclimatic building room. Several simulation scenarios are considered as a test-bed. From the obtained results, it is possible to conclude that the control system has a good behavior in several situations, i.e., it can reach the users' thermal comfort for the analyzed situations, whereas the HVAC use is adjusted through the DAP; therefore, the energy savings associated with the HVAC is increased.Spanish Ministry of Science and Innovation [DPI2014-56364-C2-1-R]; EU-ERDF funds; Competitiveness and ERDF funds; Fundacion Iberdrola Espana; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Integrated Dynamic Facade Control with an Agent-based Architecture for Commercial Buildings

Dynamic façades have significant technical potential to minimize heating, cooling, and lighting energy use and peak electric demand in the perimeter zone of commercial buildings, but the performance of these systems is reliant on being able to balance complex trade-offs between solar control, daylight admission, comfort, and view over the life of the installation. As the context for controllable energy-efficiency technologies grows more complex with the increased use of intermittent renewable energy resources on the grid, it has become increasingly important to look ahead towards more advanced approaches to integrated systems control in order to achieve optimum life-cycle performance at a lower cost. This study examines the feasibility of a model predictive control system for low-cost autonomous dynamic façades. A system architecture designed around lightweight, simple agents is proposed. The architecture accommodates whole building and grid level demands through its modular, hierarchical approach. Automatically-generated models for computing window heat gains, daylight illuminance, and discomfort glare are described. The open source Modelica and JModelica software tools were used to determine the optimum state of control given inputs of window heat gains and lighting loads for a 24-hour optimization horizon. Penalty functions for glare and view/ daylight quality were implemented as constraints. The control system was tested on a low-power controller (1.4 GHz single core with 2 GB of RAM) to evaluate feasibility. The target platform is a low-cost ($35/unit) embedded controller with 1.2 GHz dual-core cpu and 1 GB of RAM. Configuration and commissioning of the curtainwall unit was designed to be largely plug and play with minimal inputs required by the manufacturer through a web-based user interface. An example application was used to demonstrate optimal control of a three-zone electrochromic window for a south-facing zone. The overall approach was deemed to be promising. Further engineering is required to enable scalable, turnkey solutions

Simulation of a model-based optimal controller for heating systems under realistic hypothesis

An optimal controller for auxiliary heating of passive solar buildings and commercial buildings with high internal gains is tested in simulation. Some of the most restrictive simplifications that were used in previous studies of that controller (Kummert et al., 2001) are lifted: the controller is applied to a multizone building, and a detailed model is used for the HVAC system. The model-based control algorithm is not modified. It is based on a simplified internal model

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques