Identification of Evolving Rule-based Models.

An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System

Estimating uncertainty when using transient data in steady-state calculations

When using measurement data for monitoring there is often a desire for steady-state analysis. On-line condition monitoring and fault detection systems are typical applications where the traditional way of treating transient data is to remove it using methods that require tuning using thresholds. This paper suggests an alternative approach where the uncertainty estimate in a particular variable is increased in response to the presence of transients and through propagation, varies the uncertainty in the result accordingly. The formulation of the approach is described and applied to two examples from building HVAC systems. The approach is demonstrated to be a pragmatic tool that can be used to increase the robustness of calculations from time series data

Uncertainty in whole house monitoring

Monitoring energy and temperatures in dwellings is becoming commonplace due to the reduction in sensing costs. Measurements can be used for informing the occupants on their energy as well as developing better inputs for building performance simulation and verifying analysis. In a home monitoring environment making sense of this data can be difficult as the number of measurements increases; one of the key challenges for the homeowner and for organisations that collect and analysis energy data is understanding what can and cannot be ‘seen’ in the data. In building simulation, there is a growing interest in applying uncertainty to generate robust model predictions, however there is also a need to understand the uncertainties in measurements used. What is often missed in these analysis is an evaluation of the uncertainties in the measurements in relation to the intended analysis. This paper presents a set of typical domestic energy monitoring measurements that have recently been collected as part of a 4 year research project in the UK. Levels of uncertainty are evaluated and the consequences for typical metrics used in energy and comfort analysis are discussed

Uncertainty in the performance validation of HVAC Systems

A first principles based model approach to AHU cooling coil performance validation is presented. The model of correct operation is compared to that observed in the real system. In the scheme, uncertainty in the measurements and the models is evaluated to generate robust thresholds for decision making. The approach describes the design intent by estimating certain model parameters from design data and expert knowledge. The method systematically incorporates the uncertainty in these parameter estimates in the calculation of the system validation threshold. This yields a definite, transparent indication of system performance to a stated level of confidence. The approach is demonstrated on a cooling coil subsystem installed in an air-handling unit serving zones in a real building

Uncertainty in model based condition monitoring

Model based techniques for automated condition monitoring of HVAC systems have been under development for some years. The generation of false alarms has been identified as a principal factor affecting the potential usefulness of condition monitoring in HVAC applications. Results from the application of these methods to systems installed in real buildings have highlighted the difficulty in selecting good alarm thresholds that balance robustness (lack of false alarms) and sensitivity (early detection). This paper demonstrates that this balance can be met in a transparent and analytical manner, through the application of uncertainty analysis. The paper discusses the sources of uncertainty associated with component models and system measurements. A Condition Monitoring scheme applied to a typical HVAC cooling coil subsystem installed in a real building is presented. Faults are artificially introduced into the system and are used in conjunction with fault-free operation to demonstrate the sensitivity and robustness of the scheme. The principle conclusions drawn by the paper consider the likely minimum magnitudes of faults that can be detected in typical HVAC systems, without false alarm generation. More broadly however, the paper demonstrates that the issue of uncertainty affects all aspects of system monitoring, modelling and control

The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

Published UK 2050 energy scenarios specify a range of decarbonised supply side technologies combined with electrification of transportation and heating. These scenarios are designed to meet CO2 reduction targets whilst maintaining reliability of supply. Current models of the UK energy system either make significant assumptions about the role of demand side management or do not carry out the analysis at sufficient resolution and hence determining the impact of heat electrification on the reliability of supply of the scenarios is not possible. This paper presents a new model that estimates national supply and demand, hour-by-hour. Calculations are based on 11 years of weather data which allows a probabilistic assessment of deficit frequency throughout the day. It is found that achieving demand reduction targets are far more important than meeting electrification targets and that significant adoption of CHP is most likely to deliver a viable energy future for the UK

Evolutionary Synthesis of HVAC System Configurations: Algorithm Development.

This paper describes the development of an optimization procedure for the synthesis of novel heating, ventilating, and air-conditioning (HVAC) system configurations. Novel HVAC system designs can be synthesized using model-based optimization methods. The optimization problem can be considered as having three sub-optimization problems; the choice of a component set; the design of the topological connections between the components; and the design of a system operating strategy. In an attempt to limit the computational effort required to obtain a design solution, the approach adopted in this research is to solve all three sub-problems simultaneously. Further, the computational effort has been limited by implementing simplified component models and including the system performance evaluation as part of the optimization problem (there being no need in this respect to simulation the system performance). The optimization problem has been solved using a Genetic Algorithm (GA), with data structures and search operators that are specifically developed for the solution of HVAC system optimization problems (in some instances, certain of the novel operators may also be used in other topological optimization problems. The performance of the algorithm, and various search operators has been examined for a two-zone optimization problem (the objective of the optimization being to find a system design that minimizes the system energy use). In particular, the performance of the algorithm in finding feasible system designs has been examined. It was concluded that the search was unreliable when the component set was optimized, but if the component set was fixed as a boundary condition on the search, then the algorithm had an 81% probability of finding a feasible system design. The optimality of the solutions is not examined in this paper, but is described in an associated publication. It was concluded that, given a candidate set of system components, the algorithm described here provides an effective tool for exploring the novel design of HVAC systems. (c) HVAC & R journa

Estimating the air change rates in dwellings using a heat balance approach

Infiltration and ventilation rates in domestic buildings vary with construction type, weather conditions and the operation of openings in the fabric. Generating good estimates of ventilation is important for modelling, simulation and performance assessment as it has a significant impact on energy consumption. Physical tests can be applied to estimate leakage, but this is cumbersome and impractical to apply in most cases. This paper applies a heat balance approach to energy monitoring data to estimate a parameter that describes the combined ventilation and infiltration rates in real family homes. These estimates are compared with published values and a model is presented that describes the air change rate as a function of user behaviour (control of openings) and varying wind speed. The paper demonstrates that it is possible to estimate plausible air change rates from such data

Estimating the air change rates in dwellings using a heat balance approach

Infiltration and ventilation rates in domestic buildings vary with construction type, weather conditions and the operation of openings in the fabric. Generating good estimates of ventilation is important for modelling, simulation and performance assessment as it has a significant impact on energy consumption. Physical tests can be applied to estimate leakage, but this is cumbersome and impractical to apply in most cases. This paper applies a heat balance approach to energy monitoring data to estimate a parameter that describes the combined ventilation and infiltration rates in real family homes. These estimates are compared with published values and a model is presented that describes the air change rate as a function of user behaviour (control of openings) and varying wind speed. The paper demonstrates that it is possible to estimate plausible air change rates from such data

A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis

Developing sensitivity analysis (SA) that reliably and consistently identify sensitive variables can improve building performance design. In global SA, a linear regression model is normally applied to sampled-based solutions by stepwise manners, and the relative importance of variables is examined by sensitivity indexes. However, the robustness of stepwise regression is related to the choice of procedure options, and therefore influence the indication of variables’ sensitivities. This paper investigates the extent to which the procedure options of a stepwise regression for design objectives or constraints can affect variables global sensitivities, determined by three sensitivity indexes. Given that SA and optimization are often conducted in parallel, desiring for a combined method, the paper also investigates SA using both randomly generated samples and the biased solutions obtained from an optimization run. Main contribution is that, for each design objective or constraint, it is better to conclude the categories of variables importance, rather than ordering their sensitivities by a particular index. Importantly, the overall stepwise approach (with the use of bidirectional elimination,BIC, rank transformation and 100 sample size) is robust for global SA: the most important variables are always ranked on the top irrespective of the procedure options