This article introduces a methodological approach for analysing time series data from multiple sensors in order to estimate home occupancy. The approach combines the Dempster-Shafer theory, which allows the fusion of ‘evidence’ from multiple sensors, with the Hidden Markov Model. The procedure addresses some of the practicalities of occupancy estimation including the blind estimation of sensor distributions during unoccupied and occupied states, and issues of occupancy inference when some sensors have missing data. The approach is applied to preliminary data from a residential family home on the North Coast of Scotland. Features derived from sensors that monitored electrical power, dew point temperature and indoor CO2 concentration were fused and the Hidden Markov Model applied to predict the occupancy profile. The approach shown is able to predict daytime occupancy, while effectively handling periods of missing sensor data, according to cross-validation with available ground truth information. Knowledge of occupancy is then fused with consumption behaviour and a simple metric developed to allow the assessment of how likely it is that a household can participate in demand response at different periods during the day. The benefits of demand response initiatives are qualitatively discussed. The approach could be used to assist in the transition towards more active energy citizens, as envisaged by the smart grid

Emerging infrastructure for residential meter communication and data processing carries the potential to control household electrical demand within local power system constraints. Deferral of load control can be incentivized through electricity tariff price structure, which can in turn reshape a daily load profile. This paper presents a stochastic bottom-up model designed to predict the change in domestic electricity profile invoked by consumer reaction to electricity unit price, with submodels comprising user behavior, price response, and dependency between behavior and electric demand. The developed models are used to analyze the demand side management potential of the most relevant energy consuming activities through a simulated German household demonstrating that in the given scenario 8% of the annual electricity demand is shifted, leading to a 35€ annual saving. However, a 7% higher than average peak load results from the structure of the tariff signal modeled herein. A discussion on selected aspects for tariff design for categories of typical household appliances is included.

The performance of the surface zone of concrete is acknowledged as a major factor governing the rate of deterioration of reinforced concrete structures because it provides the only barrier to the ingress of water containing dissolved ionic species such as chlorides, which ultimately initiate corrosion of the reinforcement. In situ monitoring of cover-zone concrete is therefore critical in attempting to make realistic predictions as to the in-service performance of the structure. To this end, this paper presents developments in a remote interrogation system to allow for continuous, real-time monitoring of the cover-zone concrete from an office setting. Use is made of a multi electrode array embedded within cover-zone concrete to acquire discretized electrical resistivity and temperature measurements, with both parameters monitored spatially and temporally. On-site instrumentation, which allows for the remote interrogation of concrete samples placed at a marine exposure site, is detailed together wi...

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