We consider the problem of generating randomized control sequences for complex networked systems typically actuated by human agents. Our approach leverages a concept known as control improvisation, which is based on a combination of data-driven learning and controller synthesis from formal specifications. We learn from existing data a generative model (for instance, an explicit-duration hidden Markov model, or EDHMM) and then supervise this model in order to guarantee that the generated sequences satisfy some desirable specifications given in Probabilistic Computation Tree Logic (PCTL). We present an implementation of our approach and apply it to the problem of mimicking the use of lighting appliances in a residential unit, with potential applications to home security and resource management. We present experimental results showing that our approach produces realistic control sequences, similar to recorded data based on human actuation, while satisfying suitable formal requirements.
@cite_15: Abstract User behaviour plays a key role in the energy demand of residential buildings, and its importance will only increase when moving towards nearly Zero-Energy homes. However, little detailed information is available on how users interact with their homes. Due to the lack of information, user behaviour is often included in building performance simulations through one standard user pattern. To obtain more accurate energy demand simulations, user patterns are needed that capture the wide variations in behaviour without making simulations overly complicated. To this end, we developed a probabilistic model which generates realistic occupancy sequences that include three possible states: (1) at home and awake, (2) sleeping or (3) absent. This paper reports on the methodology used to construct this occupancy model based on the 2005 Belgian time-use survey. Using hierarchical clustering, we were able to identify seven typical occupancy patterns. The modelling of individual occupancy sequences based on this method enables to include highly differentiated yet realistic behaviour that is relevant to building simulations and can be used for individualised feedback based on peer comparison. The model's calibration data is available for download [1] .
@cite_1: Abstract A bottom-up modelling approach together with a set of calibration methodologies is presented to predict residential building occupants' time-dependent activities, for use in dynamic building simulations. The stochastic model to predict activity chains is calibrated using French time-use survey data (of 1998 1999), based on three types of time-dependent quantities: (i) the probability to be at home, (ii) the conditional probability to start an activity whilst being at home, and (iii) the probability distribution function for the duration of that activity. The behaviour of the individual agents in the model is first calibrated using a generic approach, where every individual is assumed to behave the same. A refinement is then presented to account for variations in the behaviours of sub-populations, having specific individual characteristics. Furthermore, a statistical approach is introduced for the modelling of transitions between two successive activity types as a Markov process. The models are then validated using a cross-validation technique, and their predictive performance is compared at an individual level, as well as for aggregated (sub-)populations.
@cite_18: Residential energy constitutes a significant portion of the total US energy consumption. Several researchers proposed energy-aware solutions for houses, promising significant energy and cost savings. However, it is important to evaluate the outcomes of these methods on larger scale, with hundreds of houses. This paper presents a human-activity based residential energy modeling framework, that can create power demand profiles considering the characteristics of household members. It constructs a mathematical model to show the detailed relationships between human activities and house power consumption. It can be used to create various house profiles with different energy demand characteristics in a reproducible manner. Comparison with real data shows that our model captures the power demand differences between different family types and accurately follows the trends seen in real data. We also show a case study that evaluates voltage deviation in a neighborhood, which requires accurate estimation of the trends in power consumption.
The related subjects of data-driven occupancy prediction @cite_15 and user behavior modeling for energy demand predictions have also been studied in recent years. In @cite_1 , a stochastic model to predict time-dependent user activity was presented, while in @cite_18 , a data-driven approach was adapted for learning residential power profiles based on user-specific factors. Integration of suitable occupancy and user prediction techniques with ours is a clear direction for future work.