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.

An Energy-Efficient DRAM Cache Architecture for Mobile Platforms With PCM-Based Main Memory

A long battery life is a first-class design objective for mobile devices, and main memory accounts for a major portion of total energy consumption. Moreover, the energy consumption from memory is expected to increase further with ever-growing demands for bandwidth and capacity. A hybrid memory system with both DRAM and PCM can be an attractive solution to provide additional capacity and reduce standby energy. Although providing much greater density than DRAM, PCM has longer access latency and limited write endurance to make it challenging to architect it for main memory. To address this challenge, this article introduces CAMP, a novel DRAM c ache a rchitecture for m obile platforms with P CM-based main memory. A DRAM cache in this environment is required to filter most of the writes to PCM to increase its lifetime, and deliver highest efficiency even for a relatively small-sized DRAM cache that mobile platforms can afford. To address this CAMP divides DRAM space into two regions: a page cache for exploiting spatial locality in a bandwidth-efficient manner and a dirty block buffer for maximally filtering writes. CAMP improves the performance and energy-delay-product by 29.2% and 45.2%, respectively, over the baseline PCM-oblivious DRAM cache, while increasing PCM lifetime by 2.7×. And CAMP also improves the performance and energy-delay-product by 29.3% and 41.5%, respectively, over the state-of-the-art design with dirty block buffer, while increasing PCM lifetime by 2.5×.

A Comparison of Energy Consumption in American Homes by Climate Region

The present research analyzes the impact of nine factors related to household demographics, building equipment, and building characteristics towards a home’s total energy consumption while controlling for climate. To do this, we have surveyed single-family owned houses from the 2015 Residential Energy Consumption Survey (RECS) dataset and controlled the analysis by Building America climate zones. Our findings are based on descriptive statistics and multiple regression models, and show that for a median-sized home in three of the five climate zones, heating equipment is still the main contributor to a household’s total energy consumed, followed by home size. Social-economic factors and building age were found relevant for some regions, but often contributed less than size and heating equipment towards total energy consumption. Water heater and education were not found to be statistically relevant in any of the regions. Finally, solar power was only found to be a significant factor in one of the regions, positively contributing to a home’s total energy consumed. These findings are helpful for policymakers to evaluate the specificities of climate regions in their jurisdiction, especially guiding homeowners towards more energy-efficient heating equipment and home configurations, such as reduced size.

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows

Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm’s effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway.

3D Modelling of Hydric Transfers in Spruce Wood with Consideration of Sorption Hysteresis

Buildings are responsible for a large portion of the total energy consumption, and have a heavy environmental impact. Wood is one of the most used bio-based building materials, as it helps reducing the environmental footprint of the construction sector. Spruce wood is widely available in France and therefore massively used in buildings. It has interesting thermal and acoustic insulation performances and a good hydric regulation property. Spruce wood microstructure is highly heterogeneous and multiphasic, which makes it harder to apprehend. On the other hand, sorption hysteresis phenomenon is responsible for the moisture accumulation in porous building materials. It is often neglected in hygrothermal transfers modelling, which leads to incorrect water content values. The aim of this work is to investigate the influence of the sorption hysteresis phenomenon on the hydric transfers of spruce wood. The heterogeneity of the microstructure is also considered through 3D tomographic reconstructions included in the modelling.

Data Mining of Regional Economic Analysis Based on Mobile Sensor Network Technology

With the continuous development of regional economy, the difference of regional economy has also aroused the attention of all walks of life. Due to the limitations of the traditional research methods, the research results are relatively simple and unable to conduct a more comprehensive analysis. The traditional methods include the following: (1) analyze the evolution of regional logistics based on the location Gini coefficient and location quotient of GIS, and reflect the situation of industrial agglomeration from the annual change curve of the location Gini coefficient; (2) use SPSS12.0 software to perform multivariate or event factors, and analyze and calculate the factor score to sum up several principal component factors; and (3) the production function analysis method is used to measure the economies of scale and agglomeration. As an extension, the relationship between the estimated total output and the agglomeration index of the factor input to measure the uniform state of the industrial distribution department is an effective measurement method for the agglomeration economy. In order to promote the sustainable development of regional economy, this paper analyzes the regional economy comprehensively based on the emerging mobile sensor network technology and data mining technology. Firstly, this paper analyzes the location technology of mobile sensor networks based on sequential Monte Carlo, selects the C -means clustering method which is suitable for economic large-sample clustering analysis, and constructs a complete data mining model. Then, the model is used to analyze the economic, social, natural, and educational science and technology indicators of a certain region from 2015 to 2019. The results show that the first principal component weight of economic indicators is the highest proportion of fiscal revenue, which is 0.986. This shows that the role of fiscal revenue in economic indicators is greater. The main index of urban consumption is 72.0, which is the highest. This shows that the population growth rate and the average consumption of urban households in social indicators play a greater role. The first principal component of natural index has the highest weight of pollution emission, which is 0.47, while the second principal component has the highest weight of total energy consumption, which is 0.48. This shows that the pollution emissions and total energy consumption in the natural indicators play a greater role. In the educational science and technology index, the first principal component weight is the highest, which is 0.61. This shows that the education funds play an important role in educational science and technology indicators. Therefore, the data mining model based on mobile sensor network technology can comprehensively and accurately analyze various indicators of regional economy.

On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks

Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes.

Passive Design Strategies for Energy Efficient Buildings in the Arabian Desert

This study aims to assess passive design features through the extensive modifications of building envelopes to affect the energy efficiency of residential buildings in hot arid climates. In support of the aim of this research, the annual electric energy bill of a typical residential building in Sharurah was collected and analyzed. Then, the DesignBuilder simulation program was used to investigate how different modifications of building envelopes could affect the energy consumption of the residential buildings under common scenarios. Varied thermal insulation, different types of glass, shading devices, and green roof were investigated with this perspective. The simulation results show that thermal insulation can significantly reduce annual energy consumption by as high as 23.6%, followed by green roofs. In contrast, shading devices and glazing system types were fewer superiors. The results also indicate that the effective combination of certain strategies can reduce total energy consumption by 35.4% relative to the base case (BC) of this research.

Empirical and Numerical Analysis of an Opaque Ventilated Facade with Windows Openings under Mediterranean Climate Conditions

In recent years, there has been growing concern regarding energy efficiency in the building sector with energy requirements increasing worldwide and now responsible for about 40% of final energy consumption in Europe. Previous research has shown that ventilated façades help to reduce energy use when cooling buildings in hot and temperate climates. Of the different ventilated façade configurations reported in the literature, the configuration of ventilated façade with window rarely has been studied, and its 3D thermodynamic behavior is deserving of further analysis and modeling. This paper examines the thermal behavior of an opaque ventilated façade with a window, in experimentally and numerical terms and its impact in energy savings to get indoor comfort. Field measurements were conducted during the winter, spring and summer seasons of 2021 using outdoor full scale test cells located in Seville (southern Spain). The modeling of the ventilated façade was carried out using a three-dimensional approach taking into account the 3D behavior of the air flow in the air cavity due to the presence of the window. The validation and comparison process using experimental data showed that the proposed model provided good results from quantitative and qualitative point of view. The reduction of the heat flux was assessed by comparing the energy performance of a ventilated façade with that of an unventilated façade. Both experimental and numerical results showed that the ventilated façade provided a reduction in annual total energy consumption when compared to the unventilated façade, being compensated the winter energy penalization by the summer energy savings. This reduction is about 21% for the whole typical climatic year showing the ability of the opaque ventilated façade studied to reduce energy consumption to insure indoor comfort, making its suitable for use in retrofitting the energy-obsolete building stock built in Spain in the middle decades of the 20 century.