Comparison between purely statistical and multi-agent based approaches for occupant behaviour modeling in buildings

National audienceThis paper analyzes two modeling approaches for occupant behaviour in buildings. It compares a purely statistical approach with a multi-agent social simulation based approach. The study concerns the door openings in an office.Ce papier analyse deux approches de modélisation du comportement d'occupants dans le bâtiment. Il compare une approche purement statistique avec une approche basée sur la simulation sociale dans un environnement multi-agent. L'étude concerne les ouvertures de porte dans un bureau

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

A data-centric stochastic model for simulation of occupant-related energy demand in buildings

If greenhouse emission reduction targets are to be met worldwide, not only will there need to be major investment in decarbonisation of the electricity supply network, but there will also need to be a significant reduction in energy demand. The built environment offers opportunities for demand reduction that can help to achieve the necessary targets. For buildings yet to be built there is a real opportunity to design low-energy environments, but the existing building stock will largely still be in existence in 50 years and so retrofit options must also be explored. No matter how efficient a building it is the occupants that drive the energy consumption - whether by requiring comfortable conditions or by using electrical equipment. While in the residential sector owner-occupiers have particular responsibility for consumption, in the non-domestic sector the financial responsibility may not lie with the building occupants and hence it is harder to target demand reduction interventions. In addition, while in a residential building the behaviour of the individual has a direct and significant impact on energy consumption, in a non-domestic building it is the collective behaviour that is important to understand. The impact of the building occupants on internal loads is critical to assessing the energy efficiency of a design or retrofit. Building energy simulation offers a means to assess the potential benefits of different options without requiring costly in-situ tests. In order for the approach to be viable, however, a simulation needs to demonstrably replicate the building performance. This has proved to be difficult not only pre-construction but even for operational demand, in part because individual and collective occupant behaviour is difficult to quantify. Typically, building energy simulation packages require occupant-related internal loads to be input into the simulation via a deterministic schedule consisting of a peak daily demand and a diversity schedule that describes how the demand varies over a 24hr period. The stochastic nature of occupant-related energy demand is well known but as yet there is no accepted methodology for generating stochastic loads for building energy simulation. A new approach is required. The aim of this thesis is to develop a new model for the definition of occupant-related building internal loads for input into building energy simulation. Early studies showed that a model must not only be able to generate good estimates of the key parameters of interest with a measure of the uncertainty, but must also be able to assimilate data, be able to simulate operational change and be straightforward to use. All buildings generate monitored data of some form, even if it is just monitored consumption for purposes of billing. Since the start of the century there has been a rapidly increasing pool of monitored data at increasing time and spatial resolutions for both residential and non-domestic buildings. Increasing monitoring of electricity consumption generates an opportunity to gain an in-depth understanding of the nature of occupant-related internal loads. The requirement for a model to be able to assimilate these data make a data-centric model a natural choice. This study focuses on non-domestic buildings and the collective stochastic behaviour of the occupants as evidenced by monitored plug loads and lighting demand. Using monitored data from four sub-metered buildings across the Cambridge University building stock a functional data analysis approach has been used to extract the underlying structure of the data in a way which facilitates generation of new data samples that encompass the observed behaviour without replication. A key assumption in simulation of non-domestic buildings is that the internal loads are in some way related to the activity that takes place in a building zone. This is problematic both because the definition of activity is indeterminate and because building sub-metering strategies rarely align with the specified activities. Deconstruction of the data allows exploration of this fundamental assumption and leads to the conclusion that activity per se is not a good indicator of internal loads. Instead, for plug loads it is the expected variability of the data that is important, whereas for lighting the control strategy of each individual building zone defines the stochasticity of the demand. The model has been developed into a practical online tool for generation of plug loads and lighting demand in the form of annual hourly time histories of internal load that can be input directly into a building energy simulation. As a design tool the modeller can select an expected level of variability in demand and use estimated base load and load range to generate synthetic demand profiles. The beauty of the approach is that if monitored data are available - for example when optimising retrofit designs - the data can be used to generate synthetic time histories that encompass observed demand but can also be modified to account for operational change - a reduction in minimum daily demand for example. Finally this thesis suggests a potential alternative to the activity-based deterministic approach for the specification of occupant-related building internal loads. Rather than generating loads for each new simulation on a case by case basis, the suggestion is to use an approach similar to that used for the specification of weather data - another stochastic input. The proposal is to create annual hourly stochastic samples of typical demand according to the expected variability. These would be used with user-defined energy use intensity values with scenarios for extreme demand in much the same way that typical and future weather scenarios are modelled. The methodology presented here is one such way to generate annual hourly stochastic sample data and provides an initial step towards the specification of such typical load profiles.Laing O'Rourk

Data-Driven Optimized Operation of Buildings with Intermittent Renewables and Application to a Net-Zero Energy Library

We are at the intersection of three major trends in the built environment where: (i) occupants' comfort, health and safety requirements are needed to support a productive workplace while maintaining a low operating cost, (ii) economic and environmental advantages are favouring an increased use of renewable energy generation and to reduce our reliance on fossil fuels, and (iii) major utilities will require regulation and are gradually shifting towards a more dynamic energy market. This thesis contributes a modelling and control framework that unifies and addresses these three points together. This thesis contributes a methodology for the development of a bootstrapped ensemble-based low-order data-driven grey-box thermal models for supervisory-level optimal controls. The model is integral to a robust sampling-based predictive control (MPC) framework. This approach is directly applicable to most commercial buildings operating on a schedule and can be extended to consider occupant-driven spaces. The methodology is applied to the Varennes Net-Zero Energy Library: Canada's first institutional net-zero energy building. Exogenous inputs are modelled to consider likely probabilistic outcomes for ambient temperature, cloudiness and interior plug loads. Bounding cases are simulated to contrast the proposed approach against conventional methods. MPC is applied to minimize various cost functions and emphasis is placed on a flexible profile-tracking cost function. The profile to track can be an open-market electrical price or a demand response signal thus improving the grid's flexibility while satisfying the building constraints and better utilizing its systems and storage. In a morning peak demand reduction case, given at least a 4-hour notice, our method is able to pre-heat the building, use minimal energy on-peak and yield the full benefits. Considering a profile tracking case to reduce grid interaction, a 10-12% total energy reduction was achieved for winter where the space was gradually heated in the morning and evening while maximizing HVAC utilization during periods of large photovoltaic generation promoting self-consumption. A similar strategy would be near-impossible to handcraft without optimization-based approaches. This proposed methodology can guide later implementations in the development of the next generation of low-cost cloud-connected controllers that are easy to deploy and can be adapted dynamically