Resolving Energy Consumption Issues and Spectrum Allocation for Future Broadband Networks

This paper investigates energy consumption patterns using panel data of final energy consumption and economic variables from 1970 to 2010 for more than 100 countries and regions. Although previous economic studies address the role of economic development and income as determinant factors of energy consumption, we examine how physical factors such as climate affects energy consumption level and patterns. By integrating economic and engineering approaches, we demonstrate statistical analyses showing that climate determines not only energy consumption levels, but also the relationship between energy consumption and income levels over time. While climate affects energy consumption patterns, our results suggest that energy technology and price policy are significant in determining whether a country can decouple energy consumption from economic growth.

The aim of this study survey is to analyze energy-efficient smart home load forecasting techniques and determine the usage of energy or power with high spectrum allocation in future smart home with the help of clustering in data mining. The study work starts presenting an overview of the smart home energy sector and the challenges it is facing; it is observed a change on the energy policies promoting the energy efficiency, encouraging an active role of the consumer, instructing them about the importance of the consumer behavior and protecting consumer rights. Electricity is gaining room as energy source; its share will keep increasing constantly in the following decades. In this close future, smart homes and smart meters' deployment will benefit both the utility and the consumer. In this environment, new services and new business appear, focusing on the energy management field and tools, they require specialization in fields such as, computer science, software development and data science. This study work has segmented the smart home according to the similarities of their electrical load profiles, using the proportion of energy usage per hour (%) as a common framework with analysis done in this proposed research. The objective behind this energy consumption segmentation is to be able to provide personalized recommendations to each group to reduce their energy consumption and the associated costs, fostering energy efficiency measures and improving the consumer engagement for future smart homes.

The COVID-19 pandemic caused devastation to society in 2020, forcing people all across the world to alter their lifestyles. During the pandemic, people spent more time at home, and this shift in occupancy can directly influence building energy usage. COVID-19 lockdowns hastened the transition to telework, which many predict will continue. Changes in energy usage during lockdown are thus a significant source for forecasting future energy consumption in buildings. This study aims to measure the effects of the COVID-19 lockout on home energy usage. The energy usage of a seven residential building complex in Johor Bahru, Malaysia before, during, and after the first lockdown phase was compared and analyzed. It was discovered that the initial two months in the lockdown period are the most severe energy consumption due to the tight lockdown measures implemented as reflected in energy consumption patterns. Overall energy consumption for all candidate appliances increased during and after lockdown. Still, the more significant change was that consumption occurred during the daytime rather than focused in the evening as before the lockdown. The findings provide insight into the effect of a lockdown on customer energy costs and how energy utilities may be approached during such an event.

We try to give some perspectives on likely trends in future wireless broadband networking. The treatment is an exploration of some technologies; due to the requirement to cover the topic from a broad perspective, it is not possible to go into technical details. We review current developments toward future mobile broadband networks, and then present the 4G paradigm of trying to combine heterogeneous networks, both cellular wireless hotspots and sensor networks, together with Internet protocols. We show how different networks play their own roles in the emerging infrastructure so that we can hop by hop wave the broadband network from mobile devices toward the fiber optic core network.

This study analyzes the impact of energy security and geopolitical risks on energy consumption in countries with geopolitical risk from 1985 to 2021, with the aid of financial development, economic growth, and human capital as moderating variables. Employing robust panel data econometric techniques, the analysis reveals that heightened geopolitical risks and compromised energy security significantly affect energy consumption patterns. The increase in energy security risk by 1% has a dual impact on energy consumption patterns in countries with high geopolitical risk, as renewable energy consumption increases by 0.1227%, while non-renewable energy consumption also increases by 0.113%. As geopolitical risk increases by 1%, energy consumption patterns change, decreasing renewable and non-renewable energy consumption. Specifically, renewable energy consumption decreases by 0.00010% with a 1% rise in geopolitical risk. A 1% increase in financial development significantly impacts energy consumption in these countries. The adoption of sustainable energy solutions is positively correlated with the development of financial markets in high-risk countries, with non-renewable energy consumption decreasing by 0.0187% as financial development progresses. Additionally, an increase in economic growth of 1% leads to a 0.6750% increase in renewable energy consumption, indicating a preference for renewable sources as economies expand, aligning with global sustainability efforts. The findings underscore the importance of resilient energy policies considering geopolitical uncertainties while promoting sustainable economic development. This study offers critical insights for policymakers aiming to balance energy security with financial stability in geopolitically sensitive regions, providing a comprehensive framework to guide strategic decisions in the face of evolving global risks.

This paper reviews the correlation between per capita GDP and per capita consumption of different sources of energy for OPEC Member Countries, the G-7 and three Asian countries, both with and without natural logarithm. In addition, the paper estimates the ratios for total GDP and total energy consumption of different sources of energy and also estimates GDP energy consumption elasticities. The paper concludes that on a per capita basis most OPEC Countries exhibit negative and weak relationships for all forms of energy, including electricity. For the G-7 and Asian countries, this relationship is positive and strong, with the exception of oil for G-7 countries, where there is a weak correlation. Surprisingly, most OPEC Countries showed a comparatively strong and positive correlation when tested for total GDP in relation to total energy consumption of the respective energy sources. The relationship for the rest of the countries remains unchanged. Population might have distorted the results in OPEC Countries. These results suggest that one should be cautious when drawing conclusions and not ignore the aggregate comparison, as this could otherwise lead to wrong results. For G-7 countries, there has been a significant shift in the pattern of energy consumption in relation to GDP when comparing 1960–73 and 1973–2001. All adjusted downward in the later period. However, the greatest adjustment was associated with petroleum consumption. The general conclusion is that wealth creation in G-7 countries is directly associated with the efficient use of all forms of energy. In contrast, most OPEC Countries exhibit a weaker linkage between energy consumption and economic development on a per capita basis, probably due to inefficient usage of resources or due to disproportionate distribution of wealth and thus energy usage.

Wireless sensor “motes” is small or tiny embedded systems equipped with radios for wireless communication in the networks, which depend on batteries as a power source. The development of Medium Access Control (MAC) protocols which search is for to minimize the energy consumption in the wireless sensor network. Recent contention based MAC protocols reduce energy usage by placing the radio in a low power sleep state when not sending or receiving the message. In this paper the main emphasis is on the analysis of the Contention Based MAC Protocols and energy consumption in the networks. Based on the work done by various researchers conclude that S-MAC is the backbone of all the MAC protocols for wireless sensor networks. Our proposed work investigated the energy usage and compared the performance of IEEE 802.11 MAC protocol with S-MAC protocol on different modes like without periodic sleep and with periodic sleep on different performance metrics like Remaining Energy vs Time, Energy consumption vs global packet id and Average End to End Delay vs Time. The performance of S-MAC protocol improves on the basis of duty-cycle parameter, which determines the length of sleep period in a frame and this parameter is a variable. So changing the duty cycle will change the performance of S-MAC protocol. Finally, we have used the different routing protocol with S-MAC to evaluate the energy consumption. The experimented worked done on Network Simulator Ns2- 2.34.KeywordsMedium Access ProtocolWireless Sensor NetworkIdle listeningSleep StateS-MACEnergy ConsumptionDuty cycle

The COVID-19 pandemic has generated multiple impacts. In particular, in the educational sector, the virtual class modality generated changes in the patterns of energy consumption at the institutional level; the identification of this consumption will allow us to reflect on new energy saving and efficient use strategies. In this research, we present a case study of the effects of the COVID-19 pandemic on electricity consumption in 13 state universities in Michoacán, Mexico. Electric energy consumption has been evaluated before and during the presence of the COVID-19 between 2019 and 2020. The comparative analysis estimated the reduction in energy consumption and its economic and environmental impact. The results show a considerable decrease in electricity consumption, generating an average saving of 76.24 MWh/month, which translates into an annual emission reduction from 2019 to 2020 of approximately 497 TnCO2e, and in economic terms of $8,882.25 USD each month. In general, it was identified that consumption patterns in the use of machinery and computer equipment for administrative activities were drastically reduced. If education continues in virtual or hybrid modes, energy consumption schemes will continue to decline and institutions could move towards resilient, affordable, and sustainable models of energy production and consumption.

The capacity and geographical coverage of the global communications network continue to expand. One consequence of this expansion is a steady growth in the overall energy consumption of the network. This is the first of two papers that explore the fundamental limits on energy consumption in optical communication systems and networks. The objective of these papers is to provide a framework for understanding how this growth in energy consumption can be managed. This paper (Part I) focuses on the energy consumption in optically amplified transport systems. The accompanying paper (Part II) focuses on energy consumption in networks. A key focus of both papers is an analysis of the lower bound on energy consumption. This lower bound gives an indication of the best possible energy efficiency that could ever be achieved. The lower bound on energy in transport systems is limited by the energy consumption in optical amplifiers, and in optical transmitters and receivers. The performance of an optical transport system is ultimately set by the Shannon bound on receiver sensitivity, and depends on factors such as the modulation format, fiber losses, system length, and the spontaneous noise in optical amplifiers. Collectively, these set a lower bound on the number of amplifiers required, and hence, the amplifier energy consumption. It is possible to minimize the total energy consumption of an optically amplified system by locating repeaters strategically. The lower bound on energy consumption in optical transmitters and receivers is limited by device and circuit factors. In commercial optical transport systems, the energy consumption is at least two orders of magnitude larger than the ideal lower bounds described here. The difference between the ideal lower bounds and the actual energy consumption in commercial systems is due to inefficiencies and energy overheads. A key strategy in reducing the energy consumption of optical transport systems will be to reduce these inefficiencies and overheads.

In recent years, the use of artificial intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), in combination with the Internet of Things (IoT), has gained significant attention for optimizing energy consumption in commercial buildings. With the increasing demand for energy and the rising costs of energy, there is a pressing need for efficient methods for energy management in commercial buildings. Smart energy consumption control systems that utilize machine learning algorithms and IoT devices can provide real-time data on energy usage and automate energy usage decisions in commercial buildings. In this paper, we investigate the potential of ANN and SVM-based smart energy consumption control systems in commercial buildings. We aim to analyze the impact of using these algorithms on energy consumption patterns in commercial buildings and evaluate the efficiency and effectiveness of these systems in reducing energy consumption and costs while maintaining the desired level of comfort for the occupants. Our study will focus on comparing the performance of ANN and SVM-based algorithms in terms of energy consumption reduction and cost savings. The results of this study can provide valuable insights into the application of ANN and SVM-based smart energy consumption control systems in commercial buildings and contribute to the development of more sustainable and energy-efficient buildings.

Energy consumption from the residential sector forms a large portion of the grid demand. The growing accessibility of residential load profile data presents an opportunity for improved residential load forecasting and more effective demand side management strategies. However, the stochastic nature of residential power usage limits the effectiveness of traditional load forecasting techniques. Machine learning is a tool well-suited for predicting processes containing stochastic elements. Long short-term memory (LSTM) neural networks are especially suited for predicting time-series data such as electrical load profiles. This paper investigates the efficacy of an LSTM to perform residential load forecasting. Two LSTM-based prediction strategies are compared by forecasting the load profile for a set of five simulated, periodic, temperature profiles representing a population of electric water heaters (EWH). In Method A an aggregated load profile representing a feeder-point measurement is applied to a single LSTM model to forecast the load. In Method B each simulated temperature profile, representing appliance-level measurements, is applied to its own LSTM temperature prediction. The predicted temperature profiles are combined and used to determine the load forecast. Method A outperforms Method B and is able to predict the EWH energy with a percentage error of approximately 0.76%.

Residential buildings account for significant energy consumption, creating opportunities to offer grid services. As electric utilities seek to implement effective system operation strategies, understanding residential energy consumption patterns becomes essential; However, the time intervals of load profiles measured by utilities' smart meters are typically from 15 minutes to 60 minutes. The low-resolution data make it hard to extract appliance-level load information, which is critical for providing grid services. This paper presents a load profile generator designed to produce synthetic load profiles for residential buildings that emphasizes the importance of accurate representations of realistic energy consumption patterns. The generator takes realistic low-resolution residential load measurements and weather data as inputs, producing 1-minute interval profiles that match the characteristics of the original profiles. Further, this generator can be used to populate load profiles in areas where actual measurements are limited to improve the ability of utilities to analyze their distribution systems. By providing more high-resolution residential building load profiles, this tool supports electric utilities to enhance their residential building load control strategies and improve overall grid stability.

The importance of energy consumption and environmental protection is one of the key issues of the past decade. Most of the energy consumption during the summer season in residential buildings, offices, hospitals, and hotels is related to their chiller plant. The highly variable cooling demand of the buildings connected to a hybrid chiller plant has to be distributed among absorption and vapor compression chillers to achieve higher energy efficiencies. Cooling load sharing strategies in chiller plants have a significant impact on energy consumption and consequently with more productivity and environmentally protected. This paper examines the behavior and pattern of energy consumption in a hybrid chiller plant that includes a combination of vapor compression and absorption chillers. In order to properly understanding the pattern of energy consumption, an existing mechanical room for a hospital in Tehran has been studied for six months and its energy consumption has been compared with the software modeling. This comparison includes the capacity of each chiller, the amount of energy consumed by vapor compression chillers and absorption chillers, and the percentage of their cooling energy allocation. The results indicate that the sequence of the chiller function and how they are placed in the circuit during a partial load is in the highest importance in viewpoint of energy saving.

Has COVID-19 lockdown impacted on aged care energy use and demand?

This paper aims to evaluate and characterize the impact of optimizing the operation of the HVAC system through maintaining dynamic CO2-based Demand-Controlled ventilation (DCV) on the electricity load profile and energy consumption of the sports training center of Leibniz University Hannover. The actual ventilation control scheme, in which the operation of the HVAC system is operated with a two-stage volume flow controller based on indoor CO2 concentration is improved through two steps to avoid overventilation and reduce power consumption. For this purpose, a detailed multi-zone model of the sports center and energy supply system has been developed in TRNSYS. In the first step, a multi-stage control scenario is implemented considering the occupancy schedules and indoor CO2 concentration measurement data. In the second step, based on an indoor CO2 concentration model, a predictive control scenario is developed and applied. Aiming at characterizing the influence of these operation scenarios on the power consumption of the building, the annual electricity load profiles of the simulation cases will be analyzed and compared with the actual load profile of the building based on the technical planning documents and data provided by building management system (BMS). Simulation results show that utilizing predictive CO2-based DCV leads to a reduction of the peak load electricity by almost 2 kW and the base load by 5 kW as well as decreasing the annual energy consumption by 40 %.