Implementation of the Internet of Things for Monitoring the Companys Electrical Power Consumption

This research aims to design an electric panel monitoring system using the Internet of Things technology in company buildings so that consumers can monitor real-time electricity consumption. The energy consumption monitoring method that we propose uses PM2100 by implementing a real-time monitoring function of the power consumption of a 3-phase electric panel. The monitoring system implementation results show that the value is very close to measuring the digital multimeter measuring instrument. The monitoring system produces a current measurement accuracy of 97.38% with an error of 2.62%, while the 3-phase voltage measurement error is 0.616%. This system design helps companies obtain information faster to be considered data to improve efficiency in the Company.

Regional Scale Sediment Yield Modeling Using Gis and Remote Sensing

A grid based Regional Scale Sediment Yield Model RSSYM was setup different catchments of Indus basin using coarse resolution grid data. When coarse grid data is used, the land slope decreases and sediment delivery is reduced. In this study the slope averaging effect was minimized by using the fractal analysis approach. The fractal constant (α) and fractal dimension (D) equations were correlated with the standard deviation on elevation by Zhang et al. (2000). These equations for fractal constant and fractal dimension were developed using Digital Elevation Model of 1 km2 resolution (DEM) for local topography. The slope was computed for 250 m and 50 m downscaling from 1 km2 grid. A comparison was made between fractal constant equation and fractal dimension to scale down the slope parameter. Both equations were incorporated in RSSYM. The model was applied to Phulra catchment. The results showed that fractal dimension equation gives better results as compared to fractal constant. So the fractal dimension equation was incorporated in RSSYM. The RSSYM was applied on three catchments of Indus basin namely Soan, Gariala and Phulra. For Phulra catchment of Siran river the total observed sediment was 0.304 million tons whereas the simulated sediment was 0.291 million tons. The coefficient of efficiency (COE) was 0.85 and the coefficient of determination was 0.83 which shows that there is a good correlation between the observed and simulated values. In Gariala catchment the observed sediment was 6.01 million tons whereas simulated sediment was 6.80 million tons. The coefficient of efficiency for Gariala catchment was 0.91 and the coefficient of determination observed and simulated hydrograph was 0.81. Similarly for the Soan catchment the observed sediment was 10.61 million tons and simulated sediment was 12.29 million tons. The coefficient of determination and coefficient of efficiency for this catchment was 0.95 and 0.98 respectively. The snowmelt runoff model SRM was applied to Astor catchment to test the applicability of temperature index approach for Indus basin. The terrain is difficult to measure the hydrological and hydraulics data. Most of the data was available at the outlet of the catchments. The input data included daily temperature and precipitation, dividing the catchment in to different zone depending on the elevation difference. The elevation zones were generated from the DEM of the area. The snow depletion curves were made for the snow covered area derived from the satellite data analysis on monthly basis. The model output was discharge hydrograph. The COE was 0.91 which shows that there is a good correlation between the observed and simulated values. The statistical test showed that model performance was good. The results of SRM model encouraged to use temperature index approach for snowmelt runoff estimation in the Indus basin. A snowmelt runoff model was developed using temperature index approach. The model was coupled with regional scale sediment yield model RSSYM. The coupled model was named as modified regional scale sediment yield model (MRSSYM). The MRSSYM model was applied to Astor and Gilgit catchments. For the Astor catchment total observed sediment load was 3.98 million tons whereas the simulated sediment load was 4.34 million tons. The coefficient of the model was 0.89 whereas the coefficient of determination was 0.83. Similarly for Gilgit catchment the measured and simulated sediment loads were 4.50 and 4.48 million tons respectively. The coefficient of efficiency and coefficient of determinations were 0.95 and 0.88 respectively. From the results it can be concluded that MRSSYM can be applied with confidence to various catchments of Indus basin where runoff is due to snowfall and snow melting.