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.
Other studies have taken a bottom-up approach to model residential load at the household, substation, or distribution network level, using both statistical and physics-based approaches @cite_20 @cite_19 @cite_4 @cite_7 . These models explicitly model occupancy and different occupant activities using probabilistic methods, but rely on consumer time-of-use surveys to characterize typical'' use patterns. Thus, these methods cannot accurately model use patterns at sufficiently high resolution or account for heterogeneity within the population. Instead, our approach is to implicitly learn use patterns through model parameter estimation, using only historical power consumption data.
@cite_19 @cite_4 @cite_7 @cite_20
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A model of electric residential end-use is proposed for establishing the load diagram of an area by a process of synthesis. The model follows a "bottom-up" approach, allowing construction of the relative load shape of the area, starting from knowledge of its most relevant socioeconomic and demographic characteristics, unitary energy consumption and the load profiles of individual household appliances. Several probability functions have been introduced in order to cover the close relationship existing between the demand of residential customers and the psychological and behavioral factors typical of the household; the model makes frequent use of the latter through a Monte Carlo extraction process. The model has been applied for the simulation of a residential area where field measurements of power demand had been made at 15-minute intervals and a combined mail survey had been conducted to investigate household energy usage. The paper reports the results of a comparison between recorded and predicted load profiles. > 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. A model of residential electric load is developed. It includes psychological factors which affect individual use of the various electrical appliances. Specifically, the concepts of an "availability" function which statistically estimates the number of people in a household available to use an appliance, and a "proclivity" function which gives the probability that an individual will use that appliance at any given time of day are developed. These functions are then used to drive "physical" models of the various appliances and these are incorporated into a "combined" model which is used to estimate load on a time of day basis. Results of a computer simulation of the model are compared to actual load test data.