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In this paper, we present a deployed prototype of a scalable lowcost solution providing personalised home heating advice to households. Our solution, named MyJoulo (www.myjoulo.com), uses intelligent algorithms to analyse data collected from a specially designed USB temperature logger, placed on top of the thermostat, in order to build a thermal model of the home and to infer the operational settings of the heating system. This model is then used to calculate the impact, in terms of percentage reduction in heating costs, of various interventions (such as reducing the thermostat setpoint temperature or adjusting timer settings); providing specific actionable advice to the household. The system was launched in beta form in December 2012 and registered over 750 users in its three months of operation.

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SummaryWith current trends toward embedded computer systems ' ubiquitous accessibility, connectivity, diversification, and proliferation

Abstract

Load shifting is one means whereby buildings may reduce their peak demand and provide other services to the electric grid. Current rate tariffs penalize facility peak demand, and large commercial and industrial buildings may realize cost savings by reducing this facility peak demand. The successful shifting of electric load requires some knowledge or prediction of the peak demand. This prediction is generally imperfect, and the resulting load shifting control is sub-optimal. This paper develops the optimal load shifting operations using a battery energy storage system under certain assumptions. This optimal solution is then used to develop a generalized strategy for load shifting battery control with the purpose of reducing peak 15-minute demand. This generalized strategy involves a prediction of the target demand level and a prediction of the current 15 minute period. The method whereby these predictions are made is critical to the success of load shifting. The general control logic may be used to analyze the sensitivity of load shifting to prediction error and sampling rate..

Many fault detection, optimization, and control logic methods rely on sensor feedback that assumes the system is operating at steady state conditions, despite persistent transient disturbances. While filtering and signal processing techniques can eliminate some transient effects, this paper proposes an equilibrium prediction method for first order dynamic systems using an exponential regression. This method is particularly valuable for many commercial and industrial energy system, whose dynamics are dominated by first order thermo-fluid effects. To illustrate the basic advantages of the proposed approach, Monte Carlo simulations are used. This is followed by three distinct experimental case studies to demonstrate the practical efficacy of the proposed method. First, the ability to predict the carbon dioxide level in classrooms allows for energy efficient control of the ventilation system and ensures occupant comfort. Second, predicting the optimal time to end the cool-down of an industrial sintering furnace allows for maximum part throughput and worker safety. Finally, fault detection and diagnosis methods for air conditioning systems typically use static system models; however, the transient response of many air conditioning signals may be approximated as first order, and therefore, the prediction model enables the use of static fault detection methods with transient data. In this paper, the equilibrium prediction method's performance will be quantified using both Monte Carlo simulations and case studies.

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