The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand, and an algorithm for short-term load forecasting. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. Case studies performed using sub-metered 1-minute power consumption data from several types of appliances demonstrate the effectiveness of the model for load forecasting and anomaly detection.
Approaches are generally either physics-based, statistical, or utilize a combination of both methods. Numerous detailed physics-based models derived from first principles have been developed and used for residential end-use simulation and control @cite_14 @cite_17 @cite_5 . However, the accuracy of physics-based models may suffer due to nonidealities and heterogeneity in appliance characteristics and consumer behavior.
@cite_5 @cite_14 @cite_17
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Successful model based control relies heavily on proper system identification and accurate state estimation. We present a framework for solving these problems in the context of robotic control applications. We are particularly interested in robotic manipulation tasks, which are especially hard due to the non-linear nature of contact phenomena. We developed a solution that solves both the problems of estimation and system identification jointly. We show that these two problems are difficult to solve separately in the presence of discontinuous phenomena such as contacts. The problem is posed as a joint optimization across both trajectory and model parameters and solved via Newton's method. We present several challenges we encountered while modeling contacts and performing state estimation and propose solutions within the MuJoCo physics engine. We present experimental results performed on our manipulation system consisting of 3-DOF Phantom Haptic Devices, turned into finger manipulators. Cross-validation between different datasets, as well as leave-one-out cross-validation show that our method is robust and is able to accurately explain sensory data. Realistic time-resolved data on occupant behaviour, presence and energy use are important inputs to various types of simulations, including performance of small-scale energy systems and buildings' indoor climate, use of lighting and energy demand. This paper presents a modelling framework for stochastic generation of high-resolution series of such data. The model generates both synthetic activity sequences of individual household members, including occupancy states, and domestic electricity demand based on these patterns. The activity-generating model, based on non-homogeneous Markov chains that are tuned to an extensive empirical time-use data set, creates a realistic spread of activities over time, down to a 1-min resolution. A detailed validation against measurements shows that modelled power demand data for individual households as well as aggregate demand for an arbitrary number of households are highly realistic in terms of end-use composition, annual and diurnal variations, diversity between households, short time-scale fluctuations and load coincidence. An important aim with the model development has been to maintain a sound balance between complexity and output quality. Although the model yields a high-quality output, the proposed model structure is uncomplicated in comparison to other available domestic load models. In this paper, the modeling of building end-use energy profile is comprehensively investigated. Top-down and Bottom-up approaches are discussed with a focus on the latter for better integration with occupant information. Compared to the Time-Of-Use (TOU) data used in previous Bottom-up models, this work utilizes high frequency sampled appliance power consumption data from wireless sensor network, and hence builds an appliance-data-driven probability based end-use energy profile model. ON OFF probabilities of appliances are used in this model, to build a non-homogeneous Markov Chain, compared to the duration statistics based model that is widely used in other works. The simulation results show the capability of the model to capture the diversity and variability of different categories of end-use appliance energy profile, which can further help on the design of a modern robust building power system. 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.