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.

Evolution of Solar Energy in Chile: Residential Opportunities in Arica and Parinacota

Chile has set itself to achieve Greenhouse Gas emission neutrality, with at least 70% of electricity coming from renewable energy sources by 2050. To this end, institutional and regulatory frameworks have been improved, resulting in significant progress in medium and large-scale projects. However, solar energy production at residential level and its surplus injection to all distribution networks has been very limited. This paper analyzes the evolution of the regulatory energy policies in Chile in order to contrast it with an economic evaluation of residential projects. The analysis focuses on the city of Arica, one of the highest potential regions in terms of solar energy within the country. There, a particularly low penetration of residential solar energy usage has been observed. Based on the current situation, projections are made for Arica in 2050, through the identification of barriers and opportunities on a residential scale development. According to some recommendations, there is the need to design policies that take into account the particular characteristics of each region within the country.

A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.

Semisolid Materials Processing: A Sustainability Perspective

Sustainable development is increasing in importance with restrictions on emission and carbon footprint. Similarly, both energy and resources efficiency are required, and at the same time, cost-efficiency is required. The current paper is focusing on carbon footprint, energy usage and material use efficiency of semisolid metal casting. A detailed analysis is made on the RheoMetal process, which is benchmarked to conventional HPDC casting. The analysis includes the gating system and the importance of the use of primary or secondary material. It furthermore includes a discussion of process yield and benefits based on process capability.

Leveraging Green IoT and Blockchain Technology in the Era of Transformative Digitalization

This study focuses on the role of new blockchain technology in the green IoT ecosystem, highlights essential aspects for developing a green IoT ecosystem, and investigates how blockchain technology contributes to a greener IoT environment. Data from 360 Peruvian (Latin American) SME top managers (two informants from each of 180 SMEs) were quantitatively analyzed using Smart PLS 3.0 (SEM) and SPSS V25. The purpose of this study was to look into the direct impact of attitude, knowledge, and perceived ease of use on blockchain technology adoption for green energy usage. These three parameters' indirect effects on perceived feasibility were also evaluated. The investigations demonstrated a positive and significant association between blockchain technology adoption and perceived ease of use for green energy usage. There is no typical link between green energy usage attitudes and blockchain technology adoption. Furthermore, the data revealed that perceived feasibility mediates the impact between attitude and knowledge and blockchain technology adoption for green energy usage.

Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

Modeling the Smart Factory Manufacturing Products Characteristics: The Perspective of Energy Consumption

Economic progress is built on the foundation of energy. In the industrial sector, smart factory energy consumption analysis and forecasts are crucial for improving energy consumption rates and also for creating profits. The importance of energy analysis and forecasting in an industrial environment is increasing speedily. It is a great chance to provide a technical boost to smart factories looking to reduce energy usage and produce more profit through the control and optimization modeling. It is tough to analyze energy usage and make accurate estimations of industrial energy consumption. Consequently, this study examines monthly energy consumption to identify the discrepancy between energy usages and energy needs. It depicts the link between energy consumption, demand, and various industrial goods by pattern recognition. The correlation technique is utilized in this study to figure out the link between energy usage and the weight of various materials used in product manufacturing. Next, we use the moving average approach to calculate the monthly and weekly moving averages of energy usages. The use of data-mining techniques to estimate energy consumption rates based on production is increasingly prevalent. This study uses the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to compare the actual data with forecasting data curves to enhance energy utilization. The Root Mean Square Error (RMSE) performance evaluation result for ARIMA and SARIMA is 8.70 and 10.90, respectively. Eventually, the Variable Important technique determines the smart factory’s most essential product to enhance the energy utilization rate and obtain profitable items for the smart factory.

Hybrid Scheduling Strategy in Cloud Computing based on Optimization Algorithms

As a platform for offering on-demand services, cloud computing has increased in relevance and appeal. It has a pay-per-use model for its services. A cloud service provider's primary goal is to efficiently use resources by reducing execution time, cost, and other factors while increasing profit. As a result, effective scheduling algorithms remain a key issue in cloud computing, and this topic is categorized as an NP-complete problem. Researchers previously proposed several optimization techniques to address the NP-complete problem, but more work is needed in this area. This paper provides an overview of strategy for successful task scheduling based on a hybrid heuristic approach for both regular and larger workloads. The previous method handles the jobs adequately, but its performance degrades as the task size becomes larger. The proposed optimum scheduling method employs two distinct techniques to select the suitable VM for the specified job. To begin, it enhances the LJFP method by employing OSIG, an upgraded version of the Genetic Algorithm, to choose solutions with improved fitness factors, crossover, and mutation operators. This selection returns the best machines, and PSO then chooses one for a specific job. The appropriate machine is chosen depending on several factors, including the expected execution time, current load, and energy usage. The proposed algorithm's performance is assessed using two distinct cloud scenarios with various VMs and tasks, and overall execution time and energy usage are calculated. The proposed algorithm outperforms existing techniques in terms of energy and average execution time usage in both scenarios.