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A stochastic computer model for heating and cooling loads

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TLDR
In this article, the authors review the concept of demand-side management, particularly in regard to utility intervention into the setpoint adjustment of customers' thermostats for heating and cooling loads.
Abstract
The authors review the concept of demand-side management, particularly in regard to utility intervention into the setpoint adjustment of customers' thermostats for heating and/or cooling loads. As part of a larger effort to develop a large simulation to evaluate performance of various management strategies, they have created a simple, realistic, easily implementable model for the temperature of a house having a heater/air-conditioner which is regulated by a thermostat and operates in an environment that contains random elements. Two models previously given in the literature are reviewed before giving the details of the model, the feature of which is that it is a stochastic model operating in discrete time. Computer results show good agreement with basic physical principles, and suggest that the probability distributions of on-time and off-time are negative binomial. It is concluded that this model will provide a useful tool in the evaluation of load-management strategies. >

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Citations
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Proceedings ArticleDOI

Learning to Shift Thermostatically Controlled Loads

TL;DR: An online learning algorithm that performs demand response while learning the model of an aggregation of thermostatically controlled loads is proposed and achieves sub-linear regret and performs within 1% of the ideal case when the load is perfectly known.
Journal ArticleDOI

Window-Varying Particle Filter for Parameter Identification of Space Thermal Model

TL;DR: A modified particle filter algorithm, called window-varying particle filter (WVPF), is proposed to identify time-variesing parameters of space thermal model by adaptively changing the prediction time window in this paper.
Proceedings ArticleDOI

A new technique for short-term residential electric load forecasting including weather and lifestyle influences

TL;DR: In this article, a stochastic physically based residential load forecasting technique that accounts for random influences on the load is proposed, which is based on forecasting the weather-dependent and weather-independent components separately.
Journal ArticleDOI

Human in the loop heterogeneous modelling of thermostatically controlled loads for Demand Side Management studies.

TL;DR: A bottom up detailed model has been developed starting from thermal modelling to include external factors as well as their detrimental effect in heterogeneity using cold loads as a case study and it is found that the duty cycle of some TCLs in the population can change significantly and thus the state of the TPLs' population as a whole.
Journal ArticleDOI

On systems with non-Markovian regime changes

TL;DR: In this article, a non-Markovian jump model is proposed and its use for the synthesis of feedback regulators is discussed, and the model is shown to be more robust to burn-in and aging phenomena.
References
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Journal ArticleDOI

Parameter estimation for continuous-time models-A survey

TL;DR: The paper reviews the progress of research on parameter estimation for continuous-time models of dynamic systems over the period 1958-1980 and includes a classification system which conforms as closely as possible to that which has arisen naturally over the past two decades.
Journal ArticleDOI

Electric load model synthesis by diffusion approximation of a high-order hybrid-state stochastic system

TL;DR: In this article, a statistical approach is used to model the dynamics of the electric demand of large aggregates of electric space heaters or air conditioners, and the homogeneons group aggregrate load model is a system of coupled ordinary, and partial differential equations (Fokker-Planck equations).
Journal ArticleDOI

Physically Based Modeling of Cold Load Pickup

TL;DR: In this paper, a general, physically based, probabilistic model of power system load has been developed for short-term prediction of system demand by composition, and accounts for lifestyle dependency and weather dependency as well as effects of other exogenous processes.
Journal ArticleDOI

Mathematical problems of modeling stochastic nonlinear dynamic systems

TL;DR: In this paper, the authors introduce the engineer to the area of stochastic differential equations, and point out the mathematical techniques and pitfalls in this area Topics discussed include continuous-time Markov processes, the Fokker-Planck-Kolmogorov equations, the Ito and Stratonovich stochastically calculi, and the problem of modeling physical systems.
Journal ArticleDOI

Demand-side management alternatives

R.M. Delgado
TL;DR: An overview of DSM alternatives can be found in this article, where the authors provide a historical reference and an understanding of the types of needs DSM can address and the benefits they can provide.
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