The energy savings potential of using dynamic external louvers in an office building

This research is aimed at exploring the influence of external dynamic louvers on the energy consumption of an office building located in Abu Dhabi-UAE. The IES-VR software was used to predict the energy consumption of a representative office module in order to evaluate the overall energy performance of employing external louvers on the south, east and west oriented façades. The use of dynamic façades was compared to another simpler method of using light-sensor controlled light dimmers. The results show that the potential energy savings using light dimming strategy only was 24.4%, 24.45% and 25.19% for the south, east and west oriented façades, respectively. The proposed dynamic louvers system with light dimming strategy achieved energy savings of 34.02%, 28.57% and 30.31% for the south, east and west orientations, respectively. Detailed analysis of the results showed that the façade's optimal static angle was −20° for the south oriented façade and 20° for the east and west oriented façades. Using these fixed optimal angles resulted in slightly lower energy savings than that of the dynamic façades. This would seem to be a good tradeoff between savings in energy running cost and the investment required to install, operate and maintain a dynamic façades system.     

Medium-term electric load forecasting using singular value decomposition

Medium-term load forecasting is an important stage in electric power system planning and operation. It is used in maintenance scheduling, and to plan for outages and major works in the power system. A new technique is proposed which uses hourly loads of successive years to predict hourly loads and peak load for the next selected time span. The proposed method implements a new combination of some existing and well established techniques. This is done by first filtering out the load trend, then applying the SVD (singular value decomposition) technique to de-noise the resulting signal. Hourly load is thus divided to three main components: a) a load trend-following component, b) a random component, and c) a de-noised component. Results of applying the technique to the Jordanian power system showed that good forecasting accuracies are attained. In addition, the proposed method outperforms the traditional exponential curve fitting method. The peak load error was found to be less than 5% using the proposed methodology. It was also found that a lag period of 4 years suits the load forecasting purposes of the Jordanian power system. The proposed method is generic and can be implemented to the hourly loads of any power system.     

Short-term forecasting of Jordanian electricity demand using particle swarm optimization

Short-term electricity demand forecasting (i.e., the prediction of hourly loads (demand) for the next hour to several days out) is one of the most important tools by which an electric utility/company plans and dispatches the loading of generating units in order to meet system demand. The accuracy of the dispatching system, which is derived from the accuracy of the forecasting algorithm used, will determine the economics of the operation of the power system. The inaccuracy or large error in the forecast simply means that load matching is not optimized and consequently the generation and transmission systems are not being operated in an efficient manner. This paper discusses and presents the results of utilizing neural network for forecasting the Jordanian electricity demand that is trained by particle swarm optimization technique, which is a new adaptive algorithm based on a social-psychological metaphor. The results of using this technique are compared with the results of using back-propagation algorithm and autoregressive moving average method.     

Snowcover Influence on Backscattering from Terrain

The effects of snowcover on the microwave backscattering from terrain in the 8-35 GHz region are examined through the analysis of experimental data and by application of a semiempirical model. The model accounts for surface backscattering contributions by the snow-air and snow-soil interfaces, and for volume backscattering contributions by the snow layer. Through comparisons of backscattering data for different terrain surfaces measured both with and without snowcover, the masking effects of snow are evaluated as a function of snow water equivalent and liquid water content. The results indicate that with dry snowcover it is not possible to discriminate between different types of ground surface (concrete, asphalt, grass, and bare ground) if the snow water equivalent is greater than about 20 cm (or a depth greater than 60 cm for a snow density of 0.3 g · cm-3). For the same density, however, if the snow is wet, a depth of 10 cm is sufficient to mask the underlying surface.     

A Backscatter Model for a Randomly Perturbed Periodic Surface

A backscatter model for a randomly perturbed periodic surface has been developed to explain backscatter measurements from a bare soil surface with row structure. It is assumed that the rowdirection effect can be included by integrating the scattering coefficient ?ss (?') due to the random roughness component over the underlying periodic component of the soil surface. It is found that the rowdirection effect is strongly dependent upon the rate-of-change of ?ss (?') with the local angle ?' and that for sinusoidal surfaces, the maximum difference between looking parallel and perpendicular to the row direction occurs around an incidence angle whose tangent is usually smaller than the slope of the periodic surface at the inflection point.     

Improved Spatial Mapping of Rainfall Events with Spaceborne SAR Imagery

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 ± 30. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community. When the methodology for extracting accurate information about soil moisture status from radar data is developed, it will prove useful in a wide variety of agronomic and hydrological investigations.     

Preparation and Characterization of Poly(2,2-dimethyl-1,3-propylene oxalate) Polymer and the Study of its Metal Uptake Behavior Toward Pb(II), Cd(II), and Hg(II) Ions

Abstract

Poly(2,2-dimethyl-1,3-propylene oxalate) was synthesized from oxalyl chloride and 2,2-dimethyl-1,3-propane diol. The polymer was characterized by inherent viscosity, FT-IR, XRD, SEM, ¹H-NMR, ¹³C-NMR, DSC, and TGA. The polymer uptake behavior towards Pb(II), Cd(II), and Hg(II) ions was studied by the batch equilibrium technique as a function of pH and contact time. The adsorption isotherms of metal ions were also investigated. Column experiments were used to determine the loading capacity and study desorption of metal ions. The polymer showed high metal-ion uptake capacity towards Pb(II), but moderate capacity towards Cd(II) and Hg(II) ions. Interestingly, the polymer was found to be highly selective for Pb(II) ions at pH 5 and 25°C. The metal ion uptake properties of the polymer show fittings for both Langmuir's and Freundlich equations. The metal-bound polymer was regenerated by treatment with 1 M HNO₃. Therefore, it may be employed for the removal of heavy metal pollutants in environmental and industrial applications.     

Degree of reactivity of two kaolinitic minerals in alkali solution using zeolitic tuff or silica sand filler

Four geopolymers were synthesized by NaOH-activation of a mixture of kaolinite (Jordanian kaolinite or Ukrainian kaolinite) and a filler (zeolitic tuff or silica sand). X-ray powder diffraction (XRD), Fourier-transform infrared spectrometry (FTIR), and solid-state magic angle spinning nuclear magnetic resonance (MAS-NMR) were employed to monitor the extent of reaction and to characterize the phases in the geopolymer. Remaining kaolinite in all produced geopolymer specimens unambiguously indicated an incomplete reaction. The ²⁹Si MAS-NMR spectra of the geopolymers revealed the presence of tetrahedral-SiO₄ whereas the ²⁷Al MAS-NMR spectra revealed the presence of both tetrahedral-AlO₄ and octahedral-AlO₆. The XRD patterns of geopolymers showed the formation of a new feldspar mineral. Replacing silica sand filler by zeolitic tuff enhanced markedly the specific surface area of the corresponding geopolymers.     

Effects of Vegetation Cover on the Microwave Radiometric Sensitivity to Soil Moisture

The reduction in sensitivity of the microwave brightness temperature to soil moisture content due to vegetation cover is analyzed using airborne observations made at 1.4 and 5 GHz. The data were acquired during six flights in 1978 over a test site near Colby, Kansas. The test site consisted of bare soil, wheat stubble, and fully mature corn fields. The results for corn indicate that the radiometric sensitivity to soil moisture S decreases in magnitude with increasing frequency and with increasing angle of incidence (relative to nadir).The sensitivity reduction factor, defined in terms of the radiometric sensitivities for bare soil and canopy-covered conditions Y=1 - Scan/ Ss was found to be equal to 0.65 for normal incidence at 1.4 GHz, and increases to 0.89 at 5 GHz. These results confirm previous conclusions that the presence of vegetation cover may pose a serious problem for soil moisture detection with passive microwave sensors.     

Effects of Vegetation Cover on the Radar Sensitivity to Soil Moisture

Measurements of the backscattering coefficient ?°, made for bare and vegetation-covered fields, are used in conjunction with a simple backscattering model to evaluate the effects of vegetation cover on the estimation accuracy of soil moisture when derived from radar observations. The results indicate that for soil moisture values below 50 percent of field capacity, the backscatter contribution of the vegetation cover limits the radar's ability to predict soil moisture with an acceptable degree of accuracy. However, for moisture values in the range between 50 and 150 percent of field capacity, the measured ?° is dominated by the soil contribution and the effects of vegetation cover become secondary in importance. It is estimated that in this upper soil moisture range, which is the primary range of interest in hydrology and agriculture, a radar soil moisture prediction algorithm would predict soil moisture with an error of less than ±15 percent of field capacity in 90 percent of the cases.