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Solar energy is an abundant source of renewable/sustainable energy, which has an enormous potential in reducing the foot print of the greenhouse gases. In this paper, we presented a modelling framework of estimating solar energy over a portion of a residential community of Sandstone in the northwest of Calgary, Canada. We calculated the actual daily incident solar radiation as a function of latitude, day of year, and possible day light hours; and also employed high-resolution remote sensing images to calculate the effective roof area for installing photovoltaic cells. Strong relationships (r2:0.91–0.98) were observed between the ground-based measurements and the modelled actual incident solar radiation at three test locations in Alberta. Over the portion of Sandstone, ~1706.49 m2roof surface area was suitable for potential installation of the photovoltaic cells. With 15% efficient photovoltaic cells, our analysis revealed that we might be able to produce significant amount (i.e., in the range of ~67–100%) of electrical energy needs of the residents of Sandstone community during the period between April and September.

Downloaded From: http://remotesensing.spiedigitallibrary.org/ on 06/23/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspxAbstract. Our objective was the determination of understory grass greening stage (GGS: defined as the date when 75% of the grass in the surrounding area of a particular location would be green) using remote sensing data over the boreal-dominant forested regions in the Canadian province of Alberta. We used moderate resolution imaging spectroradiometer (MODIS)-derived accumulated growing degree days (AGDD) and normalized difference water index (NDWI) with ground-based understory GGS observations at approximately 120 lookout tower sites during the period 2006 to 2008. During 2006, we extracted the temporal dynamics of AGDD/ NDWI at the lookout tower sites and determined the best thresholds (i.e., 90 degree-days for AGDD and 0.45 for NDWI). These AGDD/NDWI thresholds were then implemented during 2007 and 2008; and observed that AGDD had better prediction capabilities in comparison to NDWI (i.e., ∼94% and ∼65% of the incidents fall within AE2 periods or AE16 days of deviations with the ground-based understory GGS observations using AGDD and NDWI thresholds, respectively). The outcomes would potentially be useful in understanding availability of food and habitat for wildlife species/animals; microclimatic environment, composition, and diversity of plant community; and forest fire danger and fire behavior in case of fire occurrences.

As it is known from the current research and studies that travel time of vehicles is the most reliable parameters to measure the cost of a link. Cost modeling of routes has many useful applications in dynamic route selection strategies e.g. route selection for emergency vehicles. Travel time of an individual vehicle passing a road segment is a function of many parameters which includes of road geometry, traffic flow characteristics, driver»s behaviors and region-specific rules and regulations, length of the segment, number of lanes, lane width, traffic flow, traffic volume and density, average speed of vehicles, vehicles composition and the ratio of turn movements on ramps. Moreover, the average travel time of vehicles on freeways is also dependent on some parameters that vary from country to country e.g. traffic regulations, driving rules, driver»s behavior and the construction of roads. Current mathematical models and simulation software»s lack the implementation of all these parameters and hence the results of these models are mostly different from the actual. The calibration of simulation software is always required to reduce this difference. The estimation of travel time using some numerical computation or simulation software»s is not reliable. This paper describes the modeling of travel time as a cost/metric of segments on a freeway. In it, regression models are used to evaluation the travel time for a throughway segment using traffic statistics acquired from field surveys. The validity of the models is explained with their statistical significance. Regression models are equally beneficial for any region if the data sets are quite large and enough parameters are included in the model. In regression analysis, the relationships among different variables are estimated. In it, different modeling techniques are used to find the relationship between dependent and independent variables. More precisely, the analysis helps to realize how the dependent variable changes with the variation of any of the independent variables keeping other variables fixed. In this work, statistical techniques i.e. regression modeling and analysis of variance (ANOVA) has been used to evaluate the impact of each independent variable (parameters) on the travel time. Finally, a multivariate regression model is used to approximate the time of travel. The estimated time is related to the actual travel time from the real field data and the model fitness is evaluated. Our dataset constitutes of four independent parameters and one dependent parameters. Independent variable (Inputs): 1 Length of the segment 2 No of lanes in the segment 3 Flow of traffic on the segment 4 Average Speed of vehicles on the segment Dependent variable (Response) 1 Travel Time Regression analysis has been used as a statistical technique to find the response variable (travel time). In this paper it is proposed to find the travel time for a particular connection using regression models applied to actual traffic data sets collected from field surveys. Real traffic data for several segments from different freeways has acquired and analyzed them using regression models. The probable travel time was compared with actual travel time for each segment of the highway and it was found that the estimation using regression models reveals the significant level of accuracy. R-Studio is being used in the statistical analysis of the data. We computed the regression equation for the estimation of travel time for all the given four parameters (distance, lanes, flow, speed) using coefficients, β0, β1, β2, β3 and β4. Figure1 shows the actual travel time of the vehicle and the estimated travel time which is obtained by the regression analysis.

Due the movement of the sun throughout the day, the insolation level incident on the fixed panel surface varies largely. The maximum level of insolation occurs only around noon. This leads to the panel to be under-utilised. To maximise the utilisation of the panel during the day, mechanical solar tracking is used. This method not only increases the utilisation of the, but increases the power being extracted from the panel. Solar tracking using one axis tracking increases the energy yield from the solar panel by 40 percent.Extended AbstractDuring the span of a day the sun's movement has been shown in figure 1. As the day passes by, the level of incident solar radiation (insolation) changes. This change takes place due to position of the sun. The angle at which the sun's rays fall on the photovoltaic panel affects the insolation level available for the panel to convert into electrical energy. For the fixed panel, the sun's rays are not normal to plane of the panel most of the time. This causes the panel to be under-utilised. To extract more energy from the same panel, solar tracking is required. This follows the sun's movement thereby increasing the insolation level throughout the day. This increase in the insolation level is due to the fact that the angle between the normal to the solar panel and incident light is to be kept minimum. Figure 1: Sun's movement throughout the day The principle of a single axis solar tracking has been shown in figure 2. The solar tracking can be accomplished by four methods: active tracking, passive tracking, chronological tracking and manual tracking [1]. Active trackers measure the light intensity from the sun using light sensors which give signal to the controller and driving mechanism. Passive trackers commonly make use of a low boiling point compressed gas. This gas is filled in two canisters each placed in east and west directions. The heating of the fluids cause the panel to tilt over to the side with more sunshine. These will have viscous dampers to prevent excessive motion in response to wind gusts [2]. A chronological tracker uses a rotation mechanism to counteract the effect of Earth's rotation. A simple rotation mechanism, turning at a constant speed of one revolution per day or 15 degrees per hour, is adequate for many purposes, such as keeping a photovoltaic panel pointing within a few degrees of the Sun. This can easily be achieved by the use of a stepper motor control. Figure 2: Principle of single axis solar tracking The data for the insolation level and temperature for the whole year have been obtained from the NASA website for Aligarh and Doha [3]. The simulations have been run assuming that there is no condition of partial shading. For the purpose of simulation of energy output during the day, five solar panels of 250 Wp were taken in parallel to give a total of 1.25 kWp of power under STC. The energy outputs for the months throughout the year were obtained for two conditions: first for the fixed panel condition, and second for the panel with continuous one-axis solar tracking. The results have been compared and shown for Aligarh and Doha in Figs. 3 and 4 respectively. In Fig. 5, the percentage increase in the energy output for each month has been shown for both the cities. Figure 3: Daily energy yield from a 1.25 kWp solar array on a monthly basis in Aligarh Figure 4: Daily energy yield from a 1.25 kWp solar array on a monthly basis in Doha Figure 5: Increase in daily energy yield on a monthly basisReferences[1] B H Khan ‘Non-Conventional Energy Resources’ Tata McGraw Hill, 2009.[2] Kamala J. and Alex J., 2014, ‘Solar Tracking for Maximum and Economic Energy Harvesting’, Int. J. of Engg. and Tech, Vol. 5(6), pp 5030–5037.[3] NASA Surface meteorology and Solar Energy website: https://eosweb.larc.nasa.gov