Energy-aware Occupancy Scheduling

Buildings are the largest consumers of energy worldwide. Within a building, heating, ventilation and air-conditioning (HVAC) systems consume the most energy, leading to trillion dollars of electrical expenditure worldwide each year. With rising energy costs and increasingly stringent regulatory environments, improving the energy efficiency of HVAC operations in buildings has become a global concern. From a short-term economic point-of-view, with over 100 billion dollars in annual electricity expenditures, even a small percentage improvement in the operation of HVAC systems can lead to significant savings. From a long-term point-of-view, the need of fostering a smart and sustainable built environment calls for the development of innovative HVAC control strategies in buildings. In this thesis, we look at the potential for integrating building operations with room booking and occupancy scheduling. More specifically, we explore novel approaches to reduce HVAC consumption in commercial buildings, by jointly optimising the occupancy scheduling decisions (e.g. the scheduling of meetings, lectures, exams) and the building’s occupancy-based HVAC control. Our vision is to integrate occupancy scheduling with HVAC control, in such a way that the energy consumption is reduced, while the occupancy thermal comfort and scheduling requirements are addressed. We identify four unique research challenges which we simultaneously tackle in order to achieve this vision, and which form the major contributions of this thesis. Our first contribution is an integrated model that achieves high efficiency in energy reduction by fully exploiting the capability to coordinate HVAC control and occupancy scheduling. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. Existing approaches typically solve these subproblems in isolation: either scheduling occupancy given conventional control policies, or optimising HVAC control using a given occupancy schedule. From a computation standpoint, our joint problem is much more challenging than either, as HVAC models are traditionally non-linear and non-convex, and scheduling models additionally introduce discrete variables capturing the time slot and location at which each activity is scheduled. We find that substantial reduction in energy consumption can be achieved by solving the joint problem, compared to the state of the art approaches using heuristic scheduling solutions and to more naïve integrations of occupancy scheduling and occupancy-based HVAC control. Our second contribution is an approach that scales to large occupancy scheduling and HVAC control problems, featuring hundreds of activity requests across a large number of offices and rooms. This approach embeds the integrated MILP model into Large Neighbourhood Search (LNS). LNS is used to destroy part of the schedule and MILP is used to repair the schedule so as to minimise energy consumption. Given sets of occupancy schedules with different constrainedness and sets of buildings with varying thermal response, our model is sufficiently scalable to provide instantaneous and near-optimal solutions to problems of realistic size, such as those found in university timetabling. The third contribution is an online optimisation approach that models and solves the online joint HVAC control and occupancy scheduling problem, in which activity requests arrive dynamically. This online algorithm greedily commits to the best schedule for the latest activity requests, but revises the entire future HVAC control strategy each time it considers new requests and weather updates. We ensure that whilst occupants are instantly notified of the scheduled time and location for their requested activity, the HVAC control is constantly re-optimised and adjusted to the full schedule and weather updates. We demonstrate that, even without prior knowledge of future requests, our model is able to produce energy-efficient schedules which are close to the clairvoyant solution. Our final contribution is a robust optimisation approach that incorporates adaptive comfort temperature control into our integrated model. We devise a robust model that enables flexible comfort setpoints, encouraging energy saving behaviors by allowing the occupants to indicate their thermal comfort flexibility, and providing a probabilistic guarantee for the level of comfort tolerance indicated by the occupants. We find that dynamically adjusting temperature setpoints based on occupants’ thermal acceptance level can lead to significant energy reduction over the conventional fixed temperature setpoints approach. Together, these components deliver a complete optimisation solution that is efficient, scalable, responsive and robust for online HVAC-aware occupancy scheduling in commercial buildings

Interval-Based Relaxation for General Numeric Planning

We generalise the interval-based relaxation to sequential numeric planning problems with non-linear conditions and effects, and cyclic dependencies. This effectively removes all the limitations on the problem placed in previous work on numeric planning heuristics, and even allows us to extend the planning language with a wider set of mathematical functions. Heuristics obtained from the generalised relaxation are pruning-safe. We derive one such heuristic and use it to solve discrete-time control-like planning problems with autonomous processes. Few planners can solve such problems, and search with our new heuristic compares favourably with the

Critical review and research roadmap of office building energy management based on occupancy monitoring

Buildings are responsible for a large portion of global energy consumption. Therefore, a detailed investigation towards a more effective energy performance of buildings is needed. Building energy performance is mature in terms of parameters related to the buildings’ physical characteristics, and their attributes are easily collectable. However, the poor ability of emulating reality pertinent to time-dependent parameters, such as occupancy parameters, may result in large discrepancies between estimated and actual energy consumption. Although efforts are being made to minimize energy waste in buildings by applying different control strategies based on occupancy information, new practices should be examined to achieve fully smart buildings by providing more realistic occupancy models to reflect their energy usage. This paper provides a comprehensive review of the methods for collection and application of occupancy-related parameters affecting total building energy consumption. Different occupancy-based control strategies are investigated with emphasis on heating, ventilation, and air conditioning (HVAC) and lighting systems. The advantages and limitations of existing methods are outlined to identify the gaps for future research

Occupant-Centric Modeling and Control for Low-Carbon and Resilient Communities

Global climate change and resulting frequent extreme weather events have highlighted the significance of energy sustainability and resilience. Communities, which refer to a group of buildings located geographically together, are important units for energy generation and consumption. Hence, the research of community energy sustainability and resilience has drawn much attention during the past decades. However, there remain many challenges surrounding community energy modeling and control to achieve the low-carbon and resilient goals. First, few tools are readily available for community-scale dynamic modeling and control-based studies. To address this gap, a community emulator was developed, which was designed to be hierarchical, scalable, and suitable for various applications. Data-driven stochastic building occupancy prediction was integrated into the emulator using logistic regression methods. Based on this work, we publicly released a library for net-zero energy community modeling using the object-oriented equation-based modeling language Modelica. Second, building load control informed by real-time carbon emission signals is underdeveloped as utility price-driven control has so far been dominant. To better facilitate community energy sustainability through decarbonization, we proposed four rule-based carbon emission responsive building control algorithms to reduce the annual carbon emissions through thermostatically controllable loads. The impact of carbon net-metering, as well as the evolvement of the future energy generation mix, is analyzed on top of both momentary and predictive rules. Based on the simulation results, the average annual household carbon emissions are decreased by 6.0% to 20.5% compared to the baseline. The average annual energy consumption is increased by less than 6.7% due to more clean hours over the year. The annual energy cost change lies between -4.1% and 3.4% on top of the baseline. Third, the enhancement of community resilience in an islanded mode through optimal operation strategies is often faced with computational challenges given the large number of controllable loads. To tackle this, we proposed a two-layer model predictive control-based resource allocation and load scheduling framework for community resilience enhancement. Within this framework, the community operator layer optimally allocates the available PV generation to each building, while the building agent layer optimally schedules controllable loads to minimize the unserved load ratio while maintaining thermal comfort. We found that the allocation process is mostly constrained by the building load flexibility. More specifically, buildings with less load flexibility tend to be allocated more PV generation than other buildings. Further, we identified the competitive relationship between the objectives of minimizing unserved load ratio and maximize comfort. Therefore, it is necessary for the building agent to have multi-objective optimization. Finally, to account for the uncertainties of occupant behavior and its impact on resilient community load scheduling, we developed a preference-aware scheduler for resilient communities. Stochastic occupant thermostat-changing behavior models were introduced into the deterministic load scheduling framework as a source of uncertainty. KRIs such as the unserved load ratio, the required battery size, and the unmet thermal preference hours were adopted to quantify the impacts. Uncertainties from occupants&rsquo; thermal preferences and their impact on load scheduling are then studied and addressed through chance constraints. Generally, the proposed controller performs better in terms of the unmet thermal preference hours and the battery sizes compared to the deterministic controller.</p

Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model

Considering the ever-growing increase in the world energy consumption and the fact that buildings contribute a large portion of the global energy consumption arises a need for detailed investigation towards more effective energy performance of buildings. Thus, monitoring, estimating, and reducing buildings’ energy consumption have always been important concerns for researchers and practitioners in the field of energy management. Since more than 80% of energy consumption happens during the operation phase of a building’s life cycle, efficient management of building operation is a promising way to reduce energy usage in buildings. Among the parameters influencing the total building energy consumption, building occupants’ presence and preferences could have high impacts on the energy usage of a building. To consider the effect of occupancy on building energy performance, different occupancy models, which aim to estimate the space utilization patterns, have been developed by researches. However, providing a comprehensive occupancy model, which could capture all important occupancy features, is still under development. Moreover, researchers investigated the effect of the application of occupancy-centered control strategies on the efficiency of the energy-consuming systems. However, there are still many challenges in this area of research mainly related to collecting, processing, and analyzing the occupancy data and the application of intelligent control strategies. In addition, generally, there is an inverse relationship between the energy consumption of operational systems and the comfort level of occupants using these systems. As a result, finding a balance between these two important concepts is crucial to improve the building operation. The optimal operation of building energy-consuming systems is a complex procedure for decision-makers, especially in terms of minimizing the energy cost and the occupants’ discomfort. On this premise, this research aims to develop a new simulation-based multi-objective optimization model of the energy consumption in open-plan offices based on occupancy dynamic profiles and occupants’ preferences and has the following objectives: (1) developing a method for extracting detailed occupancy information with varying time-steps from collected Real-Time Locating System (RTLS) occupancy data. This method captures different resolution levels required for the application of intelligent, occupancy-centered local control strategies of different building systems; (2) developing a new time-dependent inhomogeneous Markov chain occupancy prediction model based on the derived occupancy information, which distinguishes the temporal behavior of different occupants within an open-plan office; (3) improving the performance of the developed occupancy prediction model by determining the near-optimum length of the data collection period, selecting the near-optimum training dataset, and finding the most satisfying temporal resolution level for analyzing the occupancy data; (4) developing local control algorithms for building energy-consuming systems; and (5) integrating the energy simulation model of an open-plan office with an optimization algorithm to optimally control the building energy-consuming systems and to analyze the trade-off between building energy consumption and occupants’ comfort. It is found that the occupancy perdition model is able to estimate occupancy patterns of the open-plan office with 92% and 86% accuracy at occupant and zone levels, respectively. Also, the proposed integrated model improves the thermal condition by 50% along with 2% savings in energy consumption by developing intelligent, optimal, and occupancy-centered local control strategies

HVAC-Aware Occupancy Scheduling

Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approache

HVAC-Aware Occupancy Scheduling (Extended Abstract)

My research focuses on developing innovative ways to control Heating, Ventilation, and Air Conditioning (HVAC) and schedule occupancy flows in smart buildings to reduce our ecological footprint (and energy bills). We look at the potential for integrating building operations with room booking and meeting scheduling. Specifically, we improve on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. From computational standpoint, this is a challenging topic as HVAC models are inherently non-linear non-convex, and occupancy scheduling models additionally introduce discrete variables capturing the time slot and location at which each activity is scheduled. The mechanism needs to tradeoff minimizing energy cost against addressing occupancy thermal comfort and control feasibility in a highly dynamic and uncertain system