A computer study of the improvement of PV panel monthly outputs when the panel azimuth is changed thrice daily

The purpose of the calculations was to estimate the most suitable slopes and azimuths for three different positions per day of a solar panel in order to obtain the most possible energy from the PV panel compared with a stationary PV panel. The calculations were made in the computer program PV F-CHART.

Gas-filled, flat plate solar collectors

This work treats the thermal and mechanical performances of gas-filled, flat plate solar collectors in order to achieve a better performance than that of air filled collectors. The gases examined are argon, krypton and xenon which all have lower thermal conductivity than air. The absorber is formed as a tray connected to the glass. The pressure of the gas inside is near to the ambient and since the gas volume will vary as the temperature changes, there are potential risks for fatigue in the material. One heat transfer model and one mechanical model were built. The mechanical model gave stresses and information on the movements. The factors of safety were calculated from the stresses, and the movements were used as input for the heat transfer model where the thermal performance was calculated. It is shown that gas-filled, flat plate solar collectors can be designed to achieve good thermal performance at a competitive cost. The best yield is achieved with a xenon gas filling together with a normal thick absorber, where normal thick means a 0.25 mm copper absorber. However, a great deal of energy is needed to produce the xenon gas, and if this aspect is taken into account, the krypton filling is better. Good thermal performance can also be achieved using less material; a collector with a 0.1 mm thick copper absorber and the third best gas, which is argon, still gives a better operating performance than a common, commercially produced, air filled collector with a 0.25 mm absorber. When manufacturing gas-filled flat plate solar collectors, one way of decreasing the total material costs significantly, is by changing absorber material from copper to aluminium. Best yield per monetary outlay is given by a thin (0.3 mm) alu-minium absorber with an argon filling. A high factor of safety is achieved with thin absorbers, large absorber areas, rectangular constructions with long tubes and short distances between glass and absorber. The latter will also give a thin layer of gas which gives good thermal performance. The only doubtii ful construction is an argon filled collector with a normal thick (> 0.50 mm) aluminium absorber. In general, an assessment of the stresses for the proposed construction together with appropriate tests are recommended before manufacturing, since it is hard to predict the factor of safety; if one part is reinforced, some other parts can experience more stress and the factor of safety actually drops.

Simulation of Photovoltaic Panel Production as Complement to Ground Source Heat Pump System

This master thesis presents a new technological combination of two environmentally friendly sources of energy in order to provide DHW, and space heating. Solar energy is used for space heating, and DHW production using PV modules which supply direct current directly to electrical heating elements inside a water storage tank. On the other hand a GSHP system as another source of renewable energy provides heat in the water storage tank of the system in order to provide DHW and space heating. These two sources of renewable energy have been combined in this case-study in order to obtain a more efficient system, which will reduce the amount of electricity consumed by the GSHP system.The key aim of this study is to make simulations, and calculations of the amount ofelectrical energy that can be expected to be produced by a certain amount of PV modules that are already assembled on a house in Vantaa, southern Finland. This energy is then intended to be used as a complement to produce hot water in the heating system of the house beside the original GSHP system. Thus the amount of electrical energy purchased from the grid should be reduced and the compressor in the GSHP would need fewer starts which would reduce the heating cost of the GSHP system for space heating and providing hot water.The produced energy by the PV arrays in three different circuits will be charged directly to three electrical heating elements in the water storage tank of the existing system to satisfy the demand of the heating elements. The excess energy can be used to heat the water in the water storage tank to some extent which leads to a reduction of electricity consumption by the different components of the GSHP system.To increase the efficiency of the existing hybrid system, optimization of different PV configurations have been accomplished, and the results are compared. Optimization of the arrays in southern and western walls shows a DC power increase of 298 kWh/year compared with the existing PV configurations. Comparing the results from the optimization of the arrays on the western roof if the intention is to feed AC power to the components of the GSHP system shows a yearly AC power production of 1,646 kWh.This is with the consideration of no overproduction by the PV modules during the summer months. This means the optimized PV systems will be able to cover a larger part of summer demand compared with the existing system.

Thermal losses in sealed, gas-filled flat plate solar collectors

A sealed space between absorber and cover glass makes it possible reducing the influence of humidity condensate and dust at the same time as the enclosed space can be filled with a suitable gas for lowering the losses. This paper is about the size of the losses in these collectors. A calculating model of a gas-filled flat plate solar collector was built in Matlab with standard heat transfer formulas. It showed that the total loss can be reduced up to 20% when changing to an inert gas. It is also possible using a much shorter distance and still achieve low losses at the same time as the mechanical stresses in the material is reduce.

CO2 Emissions, GDP and trade : a panel cointegration approach

This paper examines the relationships among per capita CO2 emissions, per capita GDP and international trade based on panel data sets spanning the period 1960-2008: one for 150 countries and the others for sub-samples comprising OECD and Non-OECD economies. We apply panel unit root and cointegration tests, and estimate a panel error correction model. The results from the error correction model suggest that there are long-term relationships between the variables for the whole sample and for Non-OECD countries. Finally, Granger causality tests show that there is bi-directional short-term causality between per capita GDP and international trade for the whole sample and between per capita GDP and CO2 emissions for OECD countries

SOLAR PANEL MODELING ON SUITABLE ROOFS AT COLBY

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Solar Panel Modeling on Suitable Roofs at Colby

With the recent construction of Colby Green and the current plans for the construction of several new buildings, the total area for future development on campus has declined. The goal of this study was to illustrate existing campus development and to determine where future growth could occur. GIS was used in determining the different soil systems on campus, the current use of the land, and the boundaries of the Colby property. The project shows what potential obstacles the college will have in attempting to expand the campus and proposes where the best options are for construction.

A stochastic discount factor approach to asset pricing using panel data

Using the Pricing Equation, in a panel-data framework, we construct a novel consistent estimator of the stochastic discount factor (SDF) mimicking portfolio which relies on the fact that its logarithm is the ìcommon featureîin every asset return of the economy. Our estimator is a simple function of asset returns and does not depend on any parametric function representing preferences, making it suitable for testing di§erent preference speciÖcations or investigating intertemporal substitution puzzles.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise, based upon data from a well known survey is also presented. Overall, these results show promise for the feasible bias-corrected average forecast.