Vijayanarasimha H. Pakka

Real-time building occupancy sensing using neural-network based sensor network

Current occupancy sensing technologies may limit the effectiveness of buildings controls, due to a number of issues ranging from unreliable data, sensor drift, privacy concerns, and insufficient commissioning. More effective control of Heating, Ventilation and Air-conditioning (HVAC) systems may be possible using a smart and adaptive sensing network for occupancy detection, capable of turning off services out of hours, and not over-ventilating, thus enabling energy savings, and not under-ventilating during occupied periods, giving comfort and health benefits. A low-cost and non-intrusive sensor network was deployed in an open-plan office, combining information such as sound level, case temperature, carbon-dioxide (Co2) and motion, to estimate occupancy numbers, while an infrared camera was implemented to establish ground truth occupancy levels. Symmetrical uncertainty analysis was used for feature selection, and a genetic based search to evaluate an optimal sensor combination. Selected multi-sensory features were fused using a neural network. From initial results, estimation accuracy reaching up to 75% for occupied periods was achieved. The proposed system offers promising opportunities for improved comfort control and energy efficiency in buildings.

Agent-based modelling of the UK short term electricity market: Effects of intermittent wind power

This work focuses on modelling the electricity trading and market mechanism currently in place in the UK, using an agent-based approach and a learning strategy for the agents to update their bidding rules. The ongoing consultations by the Department of Energy and Climate Change on the possible models for a capacity mechanism reflect the unavoidable shift towards low-carbon and more intermittent sources of generation. One of the issues of concern is the way the system operator adapts the balancing mechanism to run in a more efficient and economical way. Here we present an agent-based model comprising two interconnected parts: a representation of the power exchange and a model of the balancing mechanism along with the settlement system. In order to assess the influence of different types of generation on the system balancing prices, we model the generating units based on the size and type of fuel involved. The agent-based model incorporates the operating decisions and control mechanisms of the system operator, and the functions of various trading entities such as generators and suppliers participating within this market. Based on this model, we report investigations into the effect of high penetrations of distributed intermittent generation in influencing the energy balancing prices.

Correlated fluctuations carry signatures of gene regulatory network dynamics

The dynamics of transcriptional control involve small numbers of molecules and result in significant fluctuations in protein and mRNA concentrations. The correlations between these intrinsic fluctuations then offer, via the fluctuation dissipation relation, the possibility of capturing the systems response to external perturbations, and hence the nature of the regulatory activity itself. We show that for simple regulatory networks of activators and repressors, the correlated fluctuations between molecular species show distinct characteristics for changes in regulatory mechanism and for changes to the topology of causal influence. Here, we do a stochastic analysis and derive time-dependent correlation functions between molecular species of regulatory networks and present analytical and numerical results on peaks and delays in correlations between proteins within networks. Upon using these values of peaks and delays as a two-dimensional feature space, we find that different regulatory mechanisms separate into distinct clusters. This indicates that experimentally observable pairwise correlations can distinguish between gene regulatory networks.

Advanced Occupancy Sensing for Energy Efficiency in Office Buildings

Control systems for heating, ventilation and air conditioning in non-domestic buildings often operate to fixed schedules, assuming maximum occupancy during business hours. Since lower occupancies usually mean less demand for heating, ventilation and air conditioning, energy savings could be made. Air quality sensing, often combined with temperature sensing, has performed sufficiently in the past for this if maintained properly, although sensor and control failures may increase energy use by as much as 50%. As energy costs increase, coupled with increased complexity in building services and reduced commissioning time, all placing ever higher demands on sensing, building controls must meet increasingly stringent environmental requirements, whilst also improving reliability. Sensor fusion offers performance and resilience to meet these demands, while cost and privacy are key factors which are also met. This article describes a neural network approach to sensor fusion for occupancy estimation. Feature selection was carried out using symmetrical uncertainty analysis, while fusion of sensor features used a back-propagation neural network, with occupant count accuracy exceeding 74%.