Harunori Yoshida

Online fault detection and diagnosis in VAV air handling unit by RARX modeling

Assimilation of cost-effective fault detection and diagnosis (FDD) technique in building management system can save enormous amount of energy and material. In this paper, recursive autoregressive exogenous algorithm is used to develop dynamic FDD model for variable air volume (VAV) air handling units. A methodology, based upon frequency response of the model is evolved for automatic fault detection and diagnosis. Results are validated with data obtained from a real building after introducing artificial faults. Robustness of the method is further established against sensor errors arising out of faulty bias during long term use or lack of proper commissioning. It is concluded that the method is quite robust and can detect and diagnose several types of faults. A short and simple method is also included in this paper to detect the faults of VAV units operating in the same zone by comparing their behavior. The new method, which requires very small amount of computation time, was tested with the aforementioned database and shows satisfactory results.

ARX and AFMM model-based on-line real-time data base diagnosis of sudden fault in AHU of VAV system

On-line diagnostic testing in automated processes requires practical fault detection and diagnosis techniques. The paper presents a model based methodology for sudden online fault detection in one of the most widely used Variable Air Volume (VAV) HVAC Systems in Commercial and Institutional Buildings. Two models, Auto Regressive Exogenous (ARX) and Adaptive Forgetting Through Multiple Models (AFMM), are trained and validated on data obtained from a real building. The models are trained using normal real time operational data and validated on data obtained by inducing a fault artificially in the damper control sub-system under normal operating conditions. It may be concluded on the basis of results obtained that the variation of parameters rather than the difference between the predicted and actual output is more prominent and reflective of the sudden fault in the system. The AFMM can detect any change in the system, i.e., when a fault was implemented and when the fault was rectified. However, it requires a long window length and, therefore, may not detect faults of low magnitude. The ARX model, on the other hand, can be used with very short window length and is more robust.

Development of parameter based fault detection and diagnosis technique for energy efficient building management system

This paper presents a complete methodology for detection and diagnosis of faults in variable air volume air handling units. Three cases are considered: (a) an off-line fault detection technique for existing buildings, (b) an automatic on-line fault detection technique for integration in building management systems (BMSs) of upcoming not very complex buildings and (c) an automatic on-line fault detection as well as diagnosis technique for BMSs of upcoming complex automated buildings. The method is based upon the auto regressive exogenous model and recursive parameter estimation algorithm. The proposed model and methodology have been trained by using several days of normal real time operational data and validated on data obtained by introducing faults artificially under normal operating conditions. It is concluded that the method is robust and can detect faults in dampers, sensors and PID control.

Climate Responsive Building Design in the Kathmandu Valley

Abstract Traditional architecture in the Kathmandu Valley is the outcome of centuries of optimization of material use, construction techniques and climate consideration. However, contemporary buildings are being built with little consideration of the climate. This study aims to explore strategies for energy efficiency and climate consciousness in modern buildings in the Kathmandu Valley. The Bioclimatic chart, Building Bioclimatic chart and Mahoney tables are used to analyse climatic parameters, and design recommendations are given based on the results of the analysis. An overview of vernacular architecture helps to understand the climatic or technological limitations of the past in order to formulate design guidelines. These guidelines provide recommendations on the orientation and layout of buildings, the size and position of openings, and the characteristics of walls and roofs.

An ARMA type weather model for air-conditioning, heating and cooling load calculation

Abstract In order to design the capacities of a heating, ventilating and air-conditioning (HVAC) systems elements, configuration of the building and the HVAC system for minimizing energy consumption, it is very important to know the air-conditioning, heating and cooling load of a building. To compute the load, weather data are very important; however, what kind of weather data should be used is a difficult problem. Conventional load calculation methods are divided into two classes, i.e., peak-load estimation and annual-load simulation. Diurnally periodic weather data are used for the peak-load estimation, but the correlation of weather elements, i.e., temperature, solar radiation, moisture contents, etc., can hardly be taken into account. Reference year weather data are used for annual-load simulation, but the results can only give the seasonal summed-up load, no information being obtained for the detailed load variations owing to the shortness of the data period. To overcome the problem, the authors constructed an ARMA-type weather model by applying a system identification technique to the original weather data. The merits of the modeling are: (1) the statistical properties of weather data are kept in the model dynamically; (2) long-term data are reduced to a small number of parameters; (3) the characteristics of weather data can be analyzed systematically; (4) even the climate of a certain location, where a precise and/or long-term data record is not available, could be modeled if the above investigations can be made at a close location; (5) the model can be used for the stochastic heating and cooling load calculation method which was developed by the authors. The methodology to build the model, examples of the weather model and the stochastic heating and cooling load results using the model are given, reasonable consistency being obtained with a simulated load.

Transient analysis of air-conditioning load and room conditions considering simultaneous heat and moisture transport of multi-layered constructions

Abstract Moisture transport in a building has a major role in condensation problems. It also has much importance in the formation of room air humidity and air-conditioning load. The focal point of this paper is to evaluate room air humidity and air-conditioning load considering moisture transport by desorption and absorption of multi-layered constructions. For this purpose, a computer code has been developed. A brief description of the calculation method and the mathematical background is given. Validation of the procedure is carried out by comparing measured and simulated humidity and temperatures. Simulated examples are shown with the results that the sorption effect keeps air humidity of a storehouse stable and increases air-conditioning loads of an ordinary office room by about 5%.

CONSIDERATION ON DECISION METHOD OF AHU CAPACITY WITH THERMAL STORAGE IN BUILDING MASS FOR HEATING

Thermal storage in building mass (TSBM), which stores thermal energy in building components such as floor slabs or structural beams by AHUs, discharges the stored thermal energy mainly in the morning and seems to be suitable to reduce large thermal load which emerges in the stand-up period of HVAC operation for heating. A field study in winter conducted in an office building located in Sapporo, Japan, shows the temperature descent of floor slabs in weekend and the cause was supposed to be the cooling by outdoor climate. It leads to the increase of stored thermal load on Monday morning, which was verified also by the increase of electrical consumption of HVAC heat source. The thermal energy supplied by AHUs in storage period was larger than that in normal operation in the field study and the AHU capacity for TSBM will be minimized when the minimum hourly thermal load of the storage period is equal to that of the normal operation period. A simulation study was conducted to show the method to minimize the AHU capacity. The study also shows that the minimum capacity brings the minimum increase of the thermal energy supplied by HVAC system.

Building HVAC systems economic performance evaluation using neural network method at beginning of design

Publisher Summary This chapter discusses how to apply neural network method to predict the economic parameters of a buildings heating ventilation and airconditioning (HVAC) systems at the beginning of the design stage. The first step of using a neural network is to train it. For the sake of training a neural network, training samples need to be determined. The training samples are a series of data vectors, every of which is consisted of input vector and output vector. For the aspect of economic characteristics of a buildings HVAC systems, the input vector contains the parameters that can influence the economic characteristics, —building area, building use, and building envelope parameter—and the output vector include economic parameters. There are near sixty different types of neural network depending on the structure and the algorithm used to define them. Among these neural network models, adaptive linear element (ADLINE) is proper to predict economic characteristics. Because ADALINE is a linear model, the influence of inconsecutive variables—building use, HVAC scheme, cooling, and heating source scheme—can only be reflected through using amending coefficient, which is determined according to the sensitivity analysis.