Green Computing practice in ICT Based Method

Green computing is the system of implementing virtual computing technology that ensure minimum energy consumption and reduces environmental waste while using computer. ICT Based Teaching and Learning (ICT-BTL) tools can be implemented for effective and quality education especially during the pandemic like Covid 19. The researchers collect the data from original sources with their personal experiences and eagerness to understand the concept in depth and the applicability for prospective mankind. The results include positive impacts of developing and implementing the green computing for ICT-BTL tools in smart class rooms. ICT experts and entrepreneurs believe in initiating the virtual classroom operations for the betterment of future and protecting from the faster growing technology era in education and research industry. The present study can be initiated for developing modern classrooms and ICT based education system with 3D presentation, demonstration of practical examples in the realistic manner.

Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.