Influence of Groves on Daylight Conditions and Visual Performance of Users of Urban Civil Infrastructures

The control and efficient use of daylight is a difficult task due to its seasonal and hourly variation. Although it is matter of active research in indoor lighting due to the necessity to light human tasks at any hour of the day in a sustainable way, little attention has been paid to the impact of daylight on visual performance, safety and ergonomics of citizens, especially pedestrians in urban areas. This attention is even lower when dealing with the interaction between daylight and urban groves, which is an essential element in cities due to a wide variety of benefits like shadowing, CO2 absorption, natural aesthetics, noise protection and many others. In this work, the interaction between daylight and typical urban trees in one city with high levels of insolation (Granada, in southern Spain) has been studied. The results, conclusions and proposals for a more sustainable urban planning are analyzed and presented.

On the Variation of Geomagnetic H-Component during Solar Quiet Days

The hourly variation of the H-component of the geometric field from two equatorial electrojet stations, Huancayo and Addis Ababa, and one non-equatorial electrojet station, Alibag, were studied to find out the trend of solar quiet variation of H for the year 2008. The dH amplitudes of the electrojet stations showed enhancement in H, while there was no enhancement in the non-electrojet station which was located far away from the dip equator. The day-to-day monthly diurnal variation was, however, observed in all the three stations. Also, at nighttime, the dH amplitudes of all the stations were non-zero which we attributed to non-ionospheric current sources like the magnetosphere since at night there was no solar radiations. For seasonal variations, an Equinoctial maximum, J-Solstitial maximum, and S-Solstitial maximum were observed in the equatorial stations while the non-equatorial station recorded an equinoctial minimum, J-solstitial minimum and D-Solstitial minimum.

Information in Electricity Forward Prices

We examine forward prices in a market where nonstorable inventory exacerbates the influence of seasonal and hourly variation in supply and demand, expected and unexpected, on the level and volatility of spot prices. We find strong evidence, unusual for a commodity, that the difference between contemporaneous forward and spot prices has power to forecast both the spot price change and the risk premium realized at delivery. Our evidence of a time-varying risk premium is consistent with expected hourly and seasonal variation in the needs of producers and retailers of electricity to hedge against extreme spot price decreases and increases, respectively.

Diurnal Variability of Soil and Air temperature as a function of time and depth, A case study of the cold wave that hit Zawia region during the period 1-5 February 1999

Soil temperature is one of the important variables in agricultural studies, which is necessary to be measured and analyze. There are some major challenges facing a lot of agricultural studies focus on soil temperature in Libya, such as, the soil temperature is measured only at Agro meteorological stations in the country, the lack of stations, the measurement of soil temperature is a cumbersome business and not as simple as air temperature be noted by single thermometer, Separate thermometers are required to measure the temperature of the soil at different depths, the installation of thermometer sensor correctly in the soil is another very complicated technical matter. However, in Libya there are no enough studies into the pattern of hourly variation in soil temperature over twenty-four hours due to the lack of such measurements at agricultural lands. So that, this attempt has been made to diagnose the behavior of soil temperature and air temperature as a case study, This work was carried out by using the daily and hourly data recorded for the temperature in the standard depths of soil (5,10,20,30,50,100 cm), and the surface air temperature at heights of (5, 10, 15, 50, 100, 150, 200 cm), Data were measured, Reported and collected every three hours interval during the period from 01 to 05 February 1999, at Zawia Agro-Meteorological Station where the location of the station was under the effect of cold wave. The data were measured on a daily basis at 0000, 0300, 0600, 0900, 1200, 1500, 1800, 2100 UTC. The correlation coefficient between the hourly soil temperature and hourly air temperature at certain depths and heights were calculated using the Pearson’s Formula, then the matrix of correlation factors have been derived, The vertical profile for the hourly variation of temperature in the layer between 100 cm under the surface of soil and 200 cm above the surface of soil have been described, Graphs and tables were done to show more explanation. The analysis of the observation shows that soil temperature variability decreases with depth, the highest temperature variability in top soil, and the lowest one is deeper than 50 cm. These results were affected due to the prevailing weather situation because at night time there is no enough solar radiation and the ambient temperature is lower than soil temperature. As a result, the soil temperature transferred heat from soil to atmosphere above. In addition, the low thermal conductivity of the soil is also one important factor that affects heat storage. These results demonstrated the importance of monitoring the soil temperature as an important element for agricultural studies. The results of the present paper could fill in some of the gap related to the soil temperature studies in Libya, and can be used for planning agronomic and plant protection practices.

IOT and Data Research In Industrial Power Management

IOT plays an important role in collecting data and machine learning for prediction in variety of applications like homecare, healthcare and energy management. In energy management there are various variables such as future power demands, generation status weather conditions and current battery status hard to expect high efficiency. Here, in this proposed idea, for higher efficiency of renewable energy, an IOT system is needed to monitor and collect these Statuses and provide energy management services. Energy will be consumed of passive operation according to hourly variation in price and battery status will be predicted by using machine learning algorithms like Logistic regression, SVM, and k-NN. We trained the system by considering five random variables in datasheet such as Current time, Current cost, predicted time, predicted cost and Solar battery status from the device. This integrated system is used for uploading power related details of Grid and Solar to IBM cloud. Depending on previous datasheet, analytics will be done by resulting which source has to be triggered to drive the load either Solar or Grid. APIs and NodeRed Tool were used for wiring sensor data and Model predicted output. In future power demands, this design will help to predict the price according to hourly variation based on the units and to trigger the source