System Integration of PV/T Collectors in Solar Cooling Systems

The demand for cooling and air-conditioning of building is increasingly ever growing. This increase is mostly due to population and economic growth in developing countries, and also desire for a higher quality of thermal comfort. Increase in the use of conventional cooling systems results in larger carbon footprint and more greenhouse gases considering their higher electricity consumption, and it occasionally creates peaks in electricity demand from power supply grid. Solar energy as a renewable energy source is an alternative to drive the cooling machines since the cooling load is generally high when solar radiation is high. This thesis examines the performance of PV/T solar collector manufactured by Solarus company in a solar cooling system for an office building in Dubai, New Delhi, Los Angeles and Cape Town. The study is carried out by analyzing climate data and the requirements for thermal comfort in office buildings. Cooling systems strongly depend on weather conditions and local climate. Cooling load of buildings depend on many parameters such as ambient temperature, indoor comfort temperature, solar gain to the building and internal gains including; number of occupant and electrical devices. The simulations were carried out by selecting a suitable thermally driven chiller and modeling it with PV/T solar collector in Polysun software. Fractional primary energy saving and solar fraction were introduced as key figures of the project to evaluate the performance of cooling system. Several parametric studies and simulations were determined according to PV/T aperture area and hot water storage tank volume. The fractional primary energy saving analysis revealed that thermally driven chillers, particularly adsorption chillers are not suitable to be utilizing in small size of solar cooling systems in hot and tropic climates such as Dubai and New Delhi. Adsorption chillers require more thermal energy to meet the cooling load in hot and dry climates. The adsorption chillers operate in their full capacity and in higher coefficient of performance when they run in a moderate climate since they can properly reject the exhaust heat. The simulation results also indicated that PV/T solar collector have higher efficiency in warmer climates, however it requires a larger size of PV/T collectors to supply the thermally driven chillers for providing cooling in hot climates. Therefore using an electrical chiller as backup gives much better results in terms of primary energy savings, since PV/T electrical production also can be used for backup electrical chiller in a net metering mechanism.

Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Data based Calibration System for Radar used by Vehicle Activated Signs

The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.