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.

Environmental sustainability assessment of wooden furniture produced in Pakistan

Life cycle assessment was carried out for a conventional wooden furniture set produced in Mardan division of the Khyber Pakhtunkhwa province of Pakistan during 2018-19. Primary data regarding inputs and outputs were collected through questionnaire surveys from 100 conventional wooden furniture set manufacturers, 50 in district Mardan and 50 in district Swabi. In the present study, cradle-to-gate life cycle assessment approach was applied for a functional unit of one conventional wooden furniture set. Production weighted average data were modelled in the environmental impacts modelling software i.e., SimaPro v.8.5. The results showed that textile used in sofa set, wood preservative for polishing and preventing insects attack and petrol used in generator had the highest contribution to all the environmental impact categories evaluated. Total cumulative energy demand for wooden furniture set manufactured was 30,005 MJ with most of the energy acquired from non-renewable fossil fuel resources.

A Data-Driven Analysis on Sustainable Energy Security

This study provides a data-driven analysis that illustrates a clear renewable energy depiction in sustainable energy security and unveils the regional issues due to the literature solely occupies energy security concept in the descriptions view, and renewable energy differences related to regions are rarely discussed. A hybrid method is proposed to valid those indicators and shows the trend of future studies. This study enriches the challenges and opportunities by contributing to understand the fundamental knowledge of renewable energy in sustainable energy security frontier, conveyance directions for future study and investigation, and assessment on global renewable energy position and regional disparities. There are valid 19 indicators, in which energy demand, energy policy, renewable resources, smart grid, and uncertainty representing the future trends. World regional comparison includes 115 countries/territories and categorized into five geographical regions. The result shows that those indicators have addressed different issues in the world regional comparison.