A Novel Home Energy Management System Environmental-based with LCA Minimization

A “smart City” includes many technological components (e.g., smarter intelligent transporting system, energy sharing, and safety and security service) that use the Internet communication technology, The Internet of Things (IoT) has an especially important role. The energy management system (EMS) is one of the main components in a smart city and is intended to conserve energy by using IoT technology. EMS has two typical parts, the building energy management system (BEMS) and home energy management system (HEMS). Both should be widely installed in buildings and the households to achieve energy conservation. This paper focuses on HEMS and presents a home energy and comfort management system that utilizes predicted mean vote (PMV) as an estimator of personal comfort by using data collected by IoT. HEMS should be implemented widely in order to effectively reduce electricity consumption; however these systems only consider a reduction in consumption. In order to create a sustainable HEMS, comfort should also be considered, because energy conservation alone is not enough to change a user's motivation for installing the system. Therefore, our system includes two processes, one is to collect environmental and physical data by using networked sensors and the other is to submit web-based questionnaires for calculating PMV values. PMV is one of the indicators of personal comfort in buildings, including homes. The other part of this system selects information from the collected data to enhance changes in personal behavior every 30 min to provide the preferred level of comfort. The information is created for each person's comfort range because different individuals have different comfort preferences. The system focuses on the information provision function of HEMS, and both energy conservation and comfort in houses at 15-min intervals. In order to test the applicability of the system, the system was installed in two test households for two weeks. In the first week, environmental and physical data were collected. This created a benchmark of electricity consumption. In the second week, information was provided. This was used to customize the comfort level for each person. In order to evaluate the system, the amount of electricity consumption reduced in the first week, and levels of comfort in the house were compared with the PMV values. As a result, the evaluation showed that the system contributed to an approximate 4.1% reduction of electricity consumption, and approximately 45.7% increase in personal comfort level.

This article proposes a multiobjective hybrid energy management system that minimizes both the electricity expenses and the household greenhouse gas emissions released due to consumption, considering the entire life cycle of the generation assets used to provide energy. The global warming potential indicator is used to decide if it is more sustainable to purchase electricity from the grid or use the household's flexible generation sources like photovoltaic panels and the energy storage system. Results prove that it is possible to reduce greenhouse gas emissions without incurring expensive electricity bill costs thanks to the hybrid-based home energy management system approach. This method gives the end-users a more influential role in the climate change solution, allowing them to give more or less importance to the economic or environmental component, according to their preferences.

Balancing the power supplies and power demands in smart home environment is key issue of today home energy management system (HEMS). In this paper, HEMS with a controller, named home energy management and control system that incorporates with admission control scheme is proposed to ensure the household power use efficiently. With introduction of "Quality of Energy Service", this paper presents a balancing power negotiation algorithm for the controller to leverage the peak power usage and guarantee the balance in between the power renewable supplies and power demands of all home appliances in the timely manner. Numerical simulation results reveal that the proposed BPN algorithm can efficiently balance the household power use in both Summer and Winter seasons.

The importance of energy management, which considers the balance between electricity supply and demand to reduce greenhouse gas (GHG) emissions in the household sector, is becoming increasingly recognized. Consequently, interest in utilizing Home Energy Management Systems (HEMS) for residential energy management is growing. HEMS visualize the energy consumption of household devices and control them to achieve energy saving and peak load reduction without compromising the services received by residents.In many regions, including Japan, research has primarily focused on verifying the electricity consumption reduction effects of HEMS introduction [1]. However, HEMS data has been mainly used for visualizing energy consumption, with limited progress in data applications and appliance control. It is essential to establish ways to utilize HEMS data to effectively achieve energy conservation in households.Household energy consumption and the usage of individual household devices depend on personal lifestyle. Therefore, it is essential to consider lifestyle patterns when implementing energy-saving measures. However, it is difficult to grasp these patterns. Although, assuming that the operation of household devices is fundamentally based on human behavior, it is possible to interpret resident behavior from the energy consumption data measured by HEMS and understand household lifestyle patterns. In other words, it is possible to estimate the lifestyle of each household without the need for additional sensors, enabling more comfortable and optimal energy-saving measures.We have begun analyzing the 30-minute interval power consumption data measured by HEMS installed in households of the "Senri-Maruyama-Hill Smart Community" in Osaka [2]. This study aims to clarify the characteristics of residents' lifestyles from HEMS data as the first step towards appliance control. Specifically, we extracted the states of rooms and home appliances using Gaussian Mixture Model (GMM) and identified state transitions with Hidden Markov Model (HMM). As a result, we were able to understand the states and frequencies of each room, such as being in a room while watching TV or being in a room without watching TV, as well as the usage times and frequencies of household appliances. Additionally, from these results, we could infer lifestyle patterns such as sleep and outings. The accuracy of these findings was evaluated through surveys conducted with the residents.The proposed method enables the extraction of lifestyle patterns solely from HEMS data, allowing for household-specific appliance control considering the lifestyle of residents. This contributes to future energy-saving measures in the household sector.

Global economic development has highlighted the issue of climate change, which is one of the most important environmental issues plaguing human beings. It is widely agreed that excessive greenhouse gas (GHG) emissions are important factors contributing to global warming. Many countries have formulated corresponding GHG emission reduction plans to deal with climate change issues. An important GHG emission source is released from sewage-sludge treatment systems. However, there has not been a comprehensive quantitative GHG emissions evaluation system in the case of sewage-sludge treatment systems, due to multiple emission sources, complex processes, and different standards. In previous studies, the Guidelines for National Greenhouse Gas Inventories (Intergovernmental Panel on Climate Change, IPCC, 2006) and Chinese Greenhouse Gas Inventory (National Center for Climate Change Strategy and International Cooperation, NCSC, 2005) were widely applied to estimate GHG emissions from sewage-sludge treatment. However, IPCC does not consider CO2 emissions from sewage treatment, and NCSC does not consider CO2 emissions from the sewage treatment and N2O emissions from sludge treatment. Therefore, the following have been conducted in this study: (1) A GHG estimation model basing on Life Cycle Thinking (LCT) was constructed, and the research objects were CH4, N2O, and CO2 that were produced by the sewage-sludge treatment system. The estimation model of CO2 and N2O, which were ignored in the IPCC report, were analyzed and discussed. The models of the GHG emission estimation were summarized and improved in the urban sewage-sludge treatment system under the different sewage-sludge treatment process scenarios. (2) The GHG emission load of major urban sewage-sludge treatment processes was analyzed, and the level and key links of environmental impacts generated by different processes were identified. This helps to understand and compare the environmental impacts of different treatment processes and provides suggestions for the sustainable development of wastewater treatment processes. (3) The GHG emission characteristics of nine scenarios of different sewage-sludge treatment processes were analyzed, and the environmental impacts caused by energy consumption and chemicals consumption were studied. Consequently, the sewage-sludge treatment process under low carbonization and low environment impact were proposed.

The production of six regionally important cellulosic biomass feedstocks, including pine, eucalyptus, unmanaged hardwoods, forest residues, switchgrass, and sweet sorghum, was analyzed using consistent life cycle methodologies and system boundaries to identify feedstocks with the lowest cost and environmental impacts. Supply chain analysis models were created for each feedstock calculating costs and supply chain requirements for the production 453,592 dry tonnes of biomass per year. Cradle-to-gate environmental impacts from these supply systems were quantified for nine mid-point indicators using SimaPro 7.2 LCA software. Conversion of grassland to managed forest for bioenergy resulted in large reductions in GHG emissions, due to carbon sequestration associated with direct land use change. However, converting forests to energy cropland resulted in large increases in GHG emissions. Production of forest-based feedstocks for biofuels resulted in lower delivered cost, lower greenhouse gas (GHG) emissions and lower overall environmental impacts than the studied agricultural feedstocks. Forest residues had the lowest environmental impact and delivered cost per dry tonne. Using forest-based biomass feedstocks instead of agricultural feedstocks would result in lower cradle-to-gate environmental impacts and delivered biomass costs for biofuel production in the southern U.S.

The main purpose of this study is to develop a sustainable smart energy management system for the desert climate, which aims to reduce energy expenses, energy consumption, and greenhouse gas (GHG) emissions via finding optimum power output and smart scheduling while considering users' uncertain behaviors. Moreover, the effectiveness of five metaheuristic optimization algorithms is analyzed and reviewed for presented system, which is modeled as a multiobjective function and contains over 1000 variables. The case study is city of Kashan located at the desert area of Iran with hot and dry climate. Presented system is established based on smart residential energy hub and home energy management system. Residential loads for a modern household are appropriately categorized and modelled. Ten different uncertain scenarios for users' energy consumption are simulated within the algorithm with considering users' comfort level simultaneously. Both energy cost and users' comfort deviation for studied multi-energy system are formulated as a multiobjective function with two weighting factors. Our results present a comparison between different cases studied and the effects of uncertain power consumption on energy cost, comfort level, and computing time. Our findings indicate that the presented system with specified weighting factors is able to reduce energy expenses around 50% in different cases. Accordingly, due to a noticeable decrease in energy consumption, GHG emissions from fossil fuels are reduced remarkably considering the fact that only 1% of Iran's power supply is provided by clean energies. Results also illustrate that considering uncertainty has more effect on users' comfort level than energy cost.

Implementation of a dynamic energy management system using real time pricing and local renewable energy generation forecasts

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact on the HEMS is assessed. The influence of changing the battery’s capacity and the PV system size on the energy costs and the lost energy are also evaluated. A significant reduction in energy costs and energy lost to the utility can be achieved by combining a suitable overnight charging level, an appropriate sample time, and an accurate forecasting tool. The HEMS has been implemented on an experimental house emulation system to demonstrate it can operate in real-time.

Opportunities to improve sustainability on commercial pasture-based dairy farms by assessing environmental impact

Home energy management (HEM) systems enable residential consumers to participate in demand response programs (DRPs) more actively. However, HEM systems confront some practical difficulties due to the uncertainty related to renewable energies as well as the uncertainty of consumers’ behavior. Moreover, the consumers aim for the highest level of comfort and satisfaction in operating their electrical appliances. In addition, technical limits of the appliances must be considered. Furthermore, DR providers aim at keeping the participation of consumers in DRPs and minimize the “response fatigue” phenomenon in the long-term period. In this paper, a stochastic model of an HEM system is proposed by considering uncertainties of electric vehicles availability and small-scale renewable energy generation. The model optimizes the customer's cost in different DRPs, while guarantees the inhabitants’ satisfaction by introducing a response fatigue index. Different case studies indicate that the implementation of the proposed stochastic HEM system can considerably decrease both the customers’ cost and response fatigue.

Appliances scheduling via cooperative multi-swarm PSO under day-ahead prices and photovoltaic generation

Increasing carbon dioxide emissions have given rise to global warming. Therefore, the need to reduce these emissions is being widely discussed. Decreasing energy consumption by efficient energy use is required to directly influence carbon dioxide emissions. Recently, energy consumption in the civilian sector is rising in Japan. To address this problem, the introduction of a Home Energy Management System (HEMS) is effective because it enables the control of power consumption and an economical energy use. Controlled heating, ventilation, and air conditioning (HVAC) is an essential function to be implemented in HEMS. HVAC control systems are now commercial and several HEMS experiments with HVAC control have been conducted in Japan. However, only few experiments have been conducted in cold districts because peak energy consumption predominantly occurs in summer. Nevertheless, from a carbon dioxide emissions reduction viewpoint, in such districts, the use of kerosene fan heaters, which are popular, becomes a dominant source of emissions around the year, but particularly in winter. In this study, we constructed a HEMS at 16 houses in a cold district, with the collecting function of environmental conditions, heating appliances status, the amount electric/oil energy consumption, and with the control function of HVAC systems. In particular, specially designed fully-controlled and monitored kerosene fan heaters are introduced. To confirm the effectiveness of the proposed HEMS, an energy saving experiment was conducted in which the use of kerosene fan heaters was controlled on the basis of the acquired environmental information, without degrading the room comfort. A reduction in carbon dioxide emissions was monitored along with the comfort level. This is the first experiment which integrates kerosene fan heaters into HEMS. This study demonstrated the ability of autonomous heater control to reduce the burden of residents and the usefulness of HEMS in Japan's cold district.

Smart homes and smart grids become today an efficient approach to face the problem of energy shortage. For example Egypt is now facing a huge problem of fuel shortage. The power stations fail to supply power to millions of electricity users. As a result the power station cuts the power on a specific district for a certain time without considering the unfair distribution of power. A Home Energy Management (HEM) system integrates the concept of Smart homes and Smart grids. This of course needs a reliable and efficient communication system that can transfer full data scheme for customer load behavior during the day 24 hours. This work proposes a prototype for a communication system that deals with this problem through implementing a web based communication link between a power manager and the heavy loads of each customer. The channel between customers and the power manager was analyzed by simulation also. A prototype of the system was completely designed, built and tested in the laboratory and the early results confirm the expected performance.

ObjectiveTo estimate, in a cohort of young Portuguese adults, the environmental impact (greenhouse gas (GHG) emissions, land use, energy used, acidification and potential eutrophication) of diet according to adherence to the Mediterranean Diet (MD).MethodsData from 1554 participants of the Epidemiologic Health Investigation of Teenagers in Porto (EPITeen) were analysed. Food intake and MD adherence were determined using validated questionnaires. The environmental impact was evaluated with the EAT-Lancet Commission tables, and the link between MD adherence and environmental impact was calculated using adjusted multivariate linear regression models.ResultsHigher adherence (high vs. low) to the MD was associated with lower environmental impact in terms of land use (7.8 vs. 8.5 m2, p = 0.002), potential acidification (57.8 vs. 62.4 g SO2-eq, p = 0.001) and eutrophication (21.7 vs. 23.5 g PO4-eq, p < 0.001). Energy use decreased only in the calorie-adjusted model (9689.5 vs. 10,265.9 kJ, p < 0.001), and GHG emissions were reduced only in a complementary model where fish consumption was eliminated (3035.3 vs. 3281.2 g CO2-eq, p < 0.001). Meat products had the greatest environmental impact for all five environmental factors analysed: 35.7% in GHG emissions, 60.9% in energy use, 72.8% in land use, 70% in acidification and 61.8% in eutrophication.ConclusionsHigher adherence to the MD is associated with lower environmental impact, particularly in terms of acidification, eutrophication, and land use. Reducing meat consumption can contribute to greater environmental sustainability.