Chris Underwood

A thermal model for photovoltaic systems

The energy balance of photovoltaic (PV) cells is modelled based on climate variables. Module temperature change is shown to be in a non-steady state with respect to time. Theoretical expressions model the energy transfer processes involved: short wave radiation, long wave radiation, convection and electrical energy production. The combined model is found to agree well with the response of the measured model temperature to transient changes in irradiance. It is found that the most precise fit to measured data is obtained by fitting the value of the forced convection coefficient for module convection. The fitted values of this coefficient were found to be within the range predicted by previous authors. Though the model is found to be accurate to within 6 K of measured temperature values 95% of the time in cloudy conditions, best accuracy is obtained in clear and overcast conditions when irradiance is subject to less fluctuation.

Building thermal model reduction using nonlinear constrained optimization

The short-time-horizon modelling of building thermal response is of relevance in situations where HVAC plant and control system analyses are of interest. In this precise area of building thermal modelling issues of model accuracy and computational efficiency become important. In this work a nonlinear constrained optimization method is used for reducing the model order of building elements. The approach involves minimizing the error between the step response of a high-order reference model whilst tuning the parameters of a lower order model in order to obtain an optimized reduced-order model. Results show that a reduced model based on a 2nd-order building element gives minimal loss of accuracy but significant improvements in computational effort when treating both high and low thermal capacity modelling problems.

Modelling methods for energy in buildings

Climate change mitigation and sustainable practices are now at the top of political and technical agendas. Environmental system modelling provides a way of appraising options and this book will make a significant contribution to the uptake of such systems. It provides knowledge of the principles involved in modelling systems, builds confidence amongst designers and offers a broad perspective of the potential of these new technologies. The aim of the book is to provide an understanding of the concepts and principles behind predictive modelling methods; review progress in the development of the modelling software available; and explore modelling in building design through international case studies based on real design problems.

Thermal comfort based fuzzy logic controller

Most heating, ventilation and air conditioning (HVAC) control systems are considered as temperature control problems. In this work, the predicted mean vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. This is achieved through the use of a fuzzy logic controller that takes into account a range of human comfort criteria in the formulation of the control action that should be applied to the heating system to bring the space to comfort conditions. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. Simulation results show that the proposed control strategy makes it possible to maximize the indoor thermal comfort and, correspondingly, a reduction in energy use of 20% was obtained for a typical 7-day winter period when compared with conventional control.

Low-order model for the simulation of a building and its heating system

Signifirant progress has been made in recent years on the development of modular and generic simulation programs for investigating the thermal behaviour of buildings and associated HVAC plant and controls. However, many of these programs are inflexible for the specific analysis of HVAC plant and control systems over short time scales. The nature of this inflexibility is discussed and a remedy is sought through the development of a low-order lumped-capacity thermal model of a building space. This is expressed as a linear time-invariant state-space description. A non-linear dynamic model of a hot water heating system with feedback control has been added and results under open-loop conditions are presented. The model has the advantage of simplicity and computational efficiency. Results are compared with field-monitored data obtained from a building in use and an excellent agreement between the two is demonstrated, though this comparison is restricted to a north-facing building space with high thermal capacity.

Application of an artificial neural network for modelling the thermal dynamics of a building's space and its heating system

Artificial neural networks (ANNs) have been used for modelling the thermal dynamics of a building’s space, its water heating system and the influence of solar radiation. A multi-layer feed-forward neural network, using a Levenberg-Marquardt backpropagation-training algorithm, has been applied to predict the future internal temperature. Real weather data for a number of winter months, together with a validated building model (based on the building constructions data), were used to train the network in order to generate a mapping between the easily measurable inputs (outdoor temperature, solar irradiance, heating valve position and the building indoor temperature) and the desired output, i.e., the predicted indoor temperature. The objective of this work was to investigate the potential of using an ANN with singular value decomposition method (SVD) to predict the indoor temperature to shut down the heating system controller early for saving the energy consumption for heating inside the building.

Generating design reference years from the UKCP09 projections and their application to future air-conditioning loads

A method is developed to generate future design reference year (DRY) data from the United Kingdom Climate Impact Programmes 2009 (UKCP09) climate change projections for a variety of future time horizons and carbon emission assumptions. The method selects three near-extreme summer months and three near-extreme winter months and weaves them into an existing test reference year (TRY). Risk levels associated with the 85th percentile (broadly equivalent to existing Chartered Institution of Building Services Engineers [CIBSE] design summer years) of the cumulative distribution function of dry-bulb temperature and, for comparison, the 99th percentile are used. A comparison is made with DRYs generated using alternative methods from other research groups. The data are applied to future air-conditioning (cooling) loads analysis for a wide range of non-domestic case study building types. Simulations using a control DRY set applied to these buildings are used to develop a simplified regression-based calculation method for predicting future air-conditioning loads. The simplified model is shown to be applicable to future weather data without loss of accuracy, which makes it possible to carry out large numbers of future cooling loads predictions without the need to perform extensive and complex energy simulations. Practical applications: It is becoming increasingly necessary to design energy and comfort services for buildings with a whole-life perspective. To assist with this, the CIBSE future weather years can be used for building simulations through to the 2080s. In June 2009, the UK’s Department of the Environment, Food and Rural Affairs (Defra) with the support of the United Kingdom Climate Impacts Programme (UKCIP) published updated climate change projections using a probabilistic method. In future, the responsibility will rest with designers to select design data from a large number of probabilistic outcomes. This work develops a technique to select design weather data called a DRY at two alternative risk levels for use in building simulations through to the 2080s. A simplified method is also proposed to allow practitioners to generate large numbers of probabilistic design cooling loads without the need to perform extensive simulations.

Anthropogenic thermogeological ‘anomaly’ in Gateshead, Tyne and Wear, UK

Abstract Two subsurface thermal profiles were measured in geothermal ‘closed-loop’ boreholes at Gateshead, Tyne and Wear, UK. They show a clear reversed gradient (temperature decreases with depth) down to at least 55 m, and the subsurface temperatures are generally warmer than those predicted purely from annual average soil temperature data and the known geothermal heat flux. This suggests that historical downward conductive heat ‘leakage’ from the long-established Gateshead urban environment has modified subsurface temperatures to depths of at least 55 m. Although poorly documented in the UK, a similar ‘urban thermogeological heat island’ effect has been noted from Canada, Sweden, Ireland and Japan.

A modelling method for building-integrated photovoltaic power supply:

Models of photovoltaic (PV) module electrical characteristics are well developed, because of the precise knowledge of the physics of semiconductor behaviour. The PV cell current-voltage characteristic is well known, and used in many models of array output. However, in many practical cases such as building-integrated photovoltaics, a model of power output only is required. Current-voltage revious researchers on which particular model should be used. This contrasts with the case of the diode model for I-V characteristics. In this work, an ef. ciency model of power output will be developed based on an adaptation of the established PV. fill factor method, and attempts are made to ground the irradiance and temperature characteristics in established theory in order to make a general PV power ef. ciency model. The model is validated using measured data from a 39.5-kW building-integrated PV array.

Robust control of HVAC plant II: controller design

A linear time-invariant model has been fitted to simulated results from the nonlinear air heating plant described in Part I. The model describes the plant well subject to uncertainty in two of its parameters. These uncertainties have been used to derive an uncertainty weight that, together with a performance weight, have led to an augmented plant description. The resulting augmented plant model was used to develop a robust controller that minimises the H- -notm of the closed-loop plant based upon the solution of two algebraic Riccati equations. The resulting controller is of the same order as the augmented (fifth-order) plant. Simulation results of the robust controller compare very favourably with a locally-optimised PID controller developed in Part I. The main advantage of the robust controller is that it merely requires a knowledge of the uncertainty limits of a simple linear model of the plant, whereas a PID controller of comparable performance would need extensive parameter tuning in practice.