S. Silva

Neural network PMV estimation for model-based predictive control of HVAC systems

Heating, Ventilating and Air Conditioning (HVAC) systems are used to provide adequate comfort to occupants of spaces within buildings. One important aspect of comfort, the thermal sensation, is commonly assessed by computation of the Predicted Mean Vote (PMV) index. Model-based predictive control may be applied to HVAC systems in existing buildings in order to provide a desired degree of thermal comfort and simultaneously achieve significant energy savings. This control strategy may be formulated as a discrete optimisation problem and solved by means of structured search techniques. Finding the optimal solution depends on the ability of computing many PMV values in a small amount of time. As the PMV formulation involves iterative computations consuming variable time, it is crucial to have a method for fast, possibly constant execution time, computation of the PMV index. In this paper it is experimentally shown that an Artificial Neural Network (ANN) can estimate the PMV index with varying degrees of efficiency over the trade-off of accuracy versus computational speed-up.

Application of Levenberg-Marquardt method to the training of spiking neural networks

One of the basic aspects of some neural networks is their attempt to approximate as much as possible their biological counterparts. The goal is to achieve a simple and robust network, easy to comprehend and capable of simulating the human brain at a computational level. This paper presents improvements to the Spikepro algorithm, by introduting a new encoding scheme, and illustrates the application of the Levenberg Marquardt algorithm to this third generation of neural network.

Energy savings in HVAC systems using discrete model-based predictive control

The paper addresses the problem of controlling an heating ventilating and air conditioning system with the purpose of achieving a desired thermal comfort level and energy savings. The formulation uses the thermal comfort as a restriction and minimises the energy spent to comply with it. This results in the maintenance of thermal comfort and on the minimisation of energy, which in most operating conditions are conflicting goals requiring some sort of optimisation method to find appropriate solutions over time. In this work a discrete model based predictive control methodology is applied to the problem. It consists of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, and experimental results obtained within a classroom will be presented, demonstrating the feasibility and performance of the approach. Finally the energy savings resulting from the application of the method are estimated.

An Intelligent Weather Station

Accurate measurements of global solar radiation, atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors (NEN) algorithm and artificial neural network (ANN) models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to the three atmospheric variables, using both approaches, over a prediction horizon of 48-steps-ahead.

Model Based Predictive Control of HVAC Systems for Human Thermal Comfort and Energy Consumption Minimisation

Abstract The problem of controlling a heating ventilating and air conditioning system in a single zone of a building is addressed. Its formulation is done in order to maintain acceptable thermal comfort for the occupants and to spend the least possible energy to achieve that. In most operating conditions these are conflicting goals, which require some sort of optimisation method to find appropriate solutions over time. In this work a model based predictive control methodology is proposed. It consists of three major components: the predictive models, implemented by radial basis function neural networks identified by means of a multi-objective genetic algorithm; the cost function that will be optimised to minimise energy consumption and provide adequate thermal comfort; and finally the optimisation method, in this case a discrete branch and bound approach. Each component will be described, and experimental results obtained within a classroom will be presented, demonstrating the feasibility and performance of the approach.

Improving a neural networks based HVAC predictive control approach

This paper improves an existing Model Based Predictive Control Approach (MBPC), applied for Heating Ventilation and Air Conditioning (HVAC) control in buildings. The existing approach uses the Predictive Mean Vote (PMV) to assess thermal comfort. It has been found that PMV estimation and forecasts deteriorate when the room is occupied. In order to solve this problem, we propose to incorporate measurements of activity inside the room in the predictive models of the inside air temperature. Another improvement to the existing approach is to use an economic cost function, reflecting the money needed for the HVAC control, instead of a cost function related with the consumption of energy.

A neural-network based intelligent weather station

Accurate measurements of global solar radiation and atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors algorithm and artificial neural network models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to three atmospheric variables, over a prediction horizon of 48-steps-ahead.