Residential Side Research Articles

Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition

Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implement with more accuracy. In this article, features of residential loads have been extracted by using the signature of the aggregated consumer’s demand. Then these features have been evaluated by methods such as logistic regression, k-nearest neighborhood, and decision tree. By assessing the results, it was determined that among the extracted features, the first two features (consumed power and injected harmonics) covered more than 89% of the variance of the entire set, and with the help of using the principal component analysis method, it was determined that by reducing the number of features to 2, a considerable amount of computation is reduced and only about 4% of the accuracy is reduced. Also, the convolutional neural network approach was used to estimate the type of load, and by identifying controllable loads and applying remote home energy management methods, it was found that by increasing participation up to 80%, more than 41% of peak-load consumption could be shifted to off-peak hours.

Land use types at the boundaries between settlements and open landscape in suburbanised settlements on the example of the Czech Republic from the perspective of the potential for planting tall vegetation

Background and aimsSettlement boundaries in suburbanised areas represent a specific environment connecting the settlements with the surrounding open landscape. Traditionally, tall vegetation (such as orchards planted in the backyards) used to form this boundary, providing a gradual transition as well as a wide range of ecosystem services. With suburbanisation, however, there appears to be a trend towards sharper boundaries between the open landscape and the settlement. The degree of the effect of suburbanisation on the boundary has, however, not been thoroughly explored yet. In this paper, the current character of such boundaries is evaluated from the perspective of land use and potential for tall vegetation. In addition, possible impacts of such changes are discussed, and measures for preventing these impacts are proposed. MethodsOn 50 suburbanised settlements in the Czech Republic, the land use types, water permeability, and suitability for the growth of tall vegetation within an 80 m belt enveloping the boundary between the settlement and open landscape were analysed using ArcGIS tools and artificial intelligence algorithms. ResultsThe dominant land use types were Residential on the inner side of the margins and Farmland on the outside of the margins, with a few meters of the transitional zone. These homogeneous land use types represented approx. 70 % of land use types at a distance of 10 m or more from the boundary. The representation of water-absorbing areas on the residential side of the boundary declined from 80 % at the settlement boundary to approx. 55 % at 40 m inside the settlement; this drop was even more pronounced for areas suitable for tall vegetation (from 90 % at 20 m outside of the boundary to 70 % at the boundary and 35 % at 40 m within the settlement). However, the potential for planting tall vegetation at the outer margins is underutilised. ConclusionsThe dominance of farmland in the immediate vicinity of the boundary between suburbanised settlements and open landscape and the underutilisation of the potential for planting tall vegetation on the outer margins deprive the settlements of ecosystem services of such ecotone and lead to the loss of habitats for wild animal and plant species.

Research on optimal dispatch model of power grid considering the uncertainty of flexible resource demand response on the residential side

AbstractResidential side flexible load participation in demand response (DR) has been widely used in recent years. However, there are uncertainties in the response willingness and response volume of residential customers, which have a non‐negligible impact on the security and economic dispatch of the power sector. This paper analyzes the uncertainty factors in the demand response process of residential users from the equipment load layer–user layer, establishes for the first time a refined model of residential users' participation in DR from the perspectives of participation uncertainty and response uncertainty, and further constructs a grid optimal dispatch model considering DR uncertainty. The experimental simulation results show that the model established in this paper is able to adjust the operation strategy according to the requirements of user comfort, enhance the enthusiasm of users to participate in DR, and at the same time reduce the dispatch security risk and economic loss caused by the uncertainty of residents' DR on the power grid.

Non-intrusive multi-label load monitoring via transfer and contrastive learning architecture

To achieve the goal of peaking carbon emissions globally and carbon neutrality, smart energy management is a promising way to boost energy conservation and estimate the residential potential for regional demand response, among which the non-intrusive load monitoring technique is highlighted due to its effectiveness on the residential side. However, the identification of multi-label appliance switching operations is still a challenge in this field, which may critically affect the total identification results due to the few-shot learning problem and the complicated overlap of features belonging to different appliances. Therefore, this paper proposed a transfer and contrastive learning architecture to identify multi-label appliances effectively. In the first stage, Gramian angular field encoding is implemented to visualize power sequences to highlight the correlation between timestamps and enhance the feature extraction efficiency. Secondly, a contrastive learning architecture is implemented to learn the general distinguishing features between samples of different labels, and density-based spatial clustering of applications with noise clustering is utilized to detect multi-label samples. Thirdly, transfer learning is utilized to enhance the multi-label identification capacity of contrastive learning structures based on the existing trained model. Finally, the effectiveness of the proposed algorithm is verified through two low-frequency non-intrusive load monitoring public datasets and real-world measurements from a pilot project in China. The results show that the proposed architecture can achieve the efficacy of deep features extraction and few-shot learning in identifying multi-label appliance switching operations.

Privacy-Based Demand Response Modeling for Residential Consumers Using Machine Learning with a Cloud–Fog-Based Smart Grid Environment

Demand response modeling in smart grids plays a significant role in analyzing and shaping the load profiles of consumers. This approach is used in order to increase the efficiency of the system and improve the performance of energy management. The use of demand response analysis in determining the load profile enhances the scheduling approach to the user profiles in the residential sector. In accordance with the behavioral pattern of the user’s profile, incentive-based demand response programs can be initiated in the residential sector. In modeling the behavioral pattern of the user’s profile, the machine learning approach is used to analyze the profile patterns. The incentive-based demand response is demonstrated in order to show the importance of maintaining the privacy of residential users, during interactions between demand- and load-profile patterns. In this work, real-time demand response modeling for residential consumers, with incentive schemes, are analyzed. The incentive schemes are proposed in order to show how the privacy of the residential units may be considered, as a result the model is developed with a two-step analysis approach. In the first step, the demand response modeling is performed with the scheduling of appliances on the residential side, by forming hubs in a cloud–fog-based smart grid environment. This process, with an incentive demand response scheme and scheduling of appliances, is performed using an optimal demand response strategy that uses a discounted stochastic game. In the second step, the privacy concerns of the demand response model from the strategy analysis are addressed using a generative adversarial network (GAN) Q-learning model and a cloud computing environment. In this work, the DR strategy model with privacy concerns for residential consumers, along with EV management, is performed in a two-step process and arrives at an optimal strategy. The efficiency and real time analysis proposed in this model are validated with real-time data analysis in simulation studies and with mathematical analysis of the proposed model.

Distributed Energy Storage Cluster Control Method for DC Microgrid Considering Flexibility

As the core support, when we develop some new energies, the energy storage industry and energy storage technology cover both the power supply, grid and the user side, residential side, and socialized functional store of the energy facilities. If we can implement the storage of energy by the distributed unit, we can save the time and space translation of energy in function and has the characteristics of flexible layout and installation at the equipment level. It is important to promote the production and consumption of renewable energy and improve the operational reliability of alternating current or direct current AC/DC hybrid power grids. It is a virtual form and develops a tendency for energy storage resources in the power grid in the future. However, as an emerging technology and resource, the use of distributed energy storage still has problems such as low efficiency, high idle rate, and single functional scenarios. Distributed energy storage systems can be used almost everywhere around the system of power, have broad application prospects and huge application potential, and will become more and more significant for the power grid in the near future. In general, the distributed energy storage system now is at a primary level of engineering application, and deep study about it still exists various key technologies which needed to lay the foundation for its popularization and application. In this paper, by constructing a microgrid experimental system containing a variety of distributed energy storage systems, research is carried out around the modeling, control, efficiency analysis, and energy management of distributed energy storage systems.

Residential Short-Term Load Forecasting during Atypical Consumption Behavior

Short-term load forecasting (STLF) is a fundamental tool for power networks’ proper functionality. As large consumers need to provide their own STLF, the residential consumers are the ones that need to be monitored and forecasted by the power network. There is a huge bibliography on all types of residential load forecast in which researchers have struggled to reach smaller forecasting errors. Regarding atypical consumption, we could see few titles before the coronavirus pandemic (COVID-19) restrictions, and afterwards all titles referred to the case of COVID-19. The purpose of this study was to identify, among the most used STLF methods—linear regression (LR), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN)—the one that had the best response in atypical consumption behavior and to state the best action to be taken during atypical consumption behavior on the residential side. The original contribution of this paper regards the forecasting of loads that do not have reference historic data. As the most recent available scenario, we evaluated our forecast with respect to the database of consumption behavior altered by different COVID-19 pandemic restrictions and the cause and effect of the factors influencing residential consumption, both in urban and rural areas. To estimate and validate the results of the forecasts, multiyear hourly residential consumption databases were used. The main findings were related to the huge forecasting errors that were generated, three times higher, if the forecasting algorithm was not set up for atypical consumption. Among the forecasting algorithms deployed, the best results were generated by ANN, followed by ARIMA and LR. We concluded that the forecasting methods deployed retained their hierarchy and accuracy in forecasting error during atypical consumer behavior, similar to forecasting in normal conditions, if a trigger/alarm mechanism was in place and there was sufficient time to adapt/deploy the forecasting algorithm. All results are meant to be used as best practices during power load uncertainty and atypical consumption behavior.

The effects of the new Feed-In Tariff Act for solar photovoltaic (PV) energy in the wake of the Fukushima accident in Japan

In 2012, the Japanese government launched the new Feed-In Tariff Act (FIT), which aimed at promoting the stable, integrated rise of renewable energy in the wake of the Fukushima nuclear accident. Solar photovoltaic (PV) energy on both the residential (installation capacity less than 10 kW) and non-residential side (installation capacity 10 kW and above) has been associated with significant benefits with the passage of the new FIT Act. However, a number of issues have emerged during implementation, including unjust enrichment from delayed PV plant operation and high renewable energy taxes. In this paper, we present an overview of the evolution of the FIT and explore how the relevant issues have been addressed through revised government policy. The paper also examines, the FIT’s effect on PV energy development. An economic analysis was conducted to identify the impact of FIT fixed price changes on the profitability of solar PV plants on both the residential and non-residential side from 2012 to 2019. Furthermore, a sensitivity analysis was conducted based on scenarios of future FIT fixed purchase price reductions and PV initial investment cost reductions. The results show that the FIT can potentially be reduced on the residential side and that the current FIT has reached its feasibility limits in terms of non-residential PV investments.

Economic Evaluation of DC Smart House Groups Considering Electric Vehicle Operation Patterns

Smart houses, which have installed on-site distributed generators such as photovoltaics and batteries and manage their own energy usage using information technology, are being studied and developed around the world. Most of the facilities in a smart house are operated as DC systems; however, conventional supply systems are AC. Moreover, the number of electric vehicles (EV), which also need DC power, have been increasing on the residential side. Therefore, a DC power supply system for smart houses can reduce the number of power converters, conversion losses, and electric charges. The initial and running costs of DC- and AC-based smart houses were calculated under various conditions in this paper. It was concluded that DC-based smart houses have lower costs than AC-based ones regardless of the AC/DC ratio in the load and in the use of EV.

Optimal energy management of residential battery storage under uncertainty

Backgroud The uncertainty associated with renewable generation and loads imposes various challenges for the economic operation of microgrids. On the residential side, energy storage systems are implemented with high expectations to enhance photovoltaic power consumption and electricity price arbitrage. Aims This paper proposes an expectation-oriented stochastic model for optimal energy management of household batteries to cope with the uncertain electricity generation and consumption. Materials & Methods The exact cost expectation of daily operation is formulated as an alternative to the conventional scenario-based average approximation. A predictive control-based battery scheduling mechanism is developed further to apply the proposed model to intraday energy storage management. Results Simulation results validate the convexity of the proposed model. Discussion Although nonlinear in nature, the resultant optimization problem is proven to be convex, which ensures computable global optimal solutions for the proposed model. Conclusion Simulation results demonstrate the effectiveness of the proposed scheduling strategy compared with existing methods.

Optimal design of home energy management strategy based on refined load model

Based on the physical model of load and the control model of expressing user’s subjective use intention, refined modeling methods are proposed for three typical high-power controllable loads on residential side, namely, heating-ventilation and air conditioning (HVAC) system, electric water heater and charging load of electric vehicle (EV) and the refined models are carried out. First of all, a custom family example is constructed, including the user’s electricity habits, structural characteristics of the house and the load, and the simulation is carried out. The refined definition of technical parameters and the influence of typical and atypical electricity consumption process on load electricity consumption effect are analyzed in detail. Then, an optimization model of energy management strategy aiming at optimizing electricity consumption cost is established based on traditional saving families and modern comfortable families. Simulation results show that the user personalized electricity consumption habits and environment have a significant impact on the control strategy of home energy management system (HEMS).

Research on modeling of electric power transaction process based on block chain technology

Block chain technology is one of the optional technologies for the realization of intelligent power transactions on the residential side, and the specific transaction process is lacking research. Based on the composition of the block chain and its technical characteristics, this paper proposes a typical transaction flow for market participants in the electricity transaction under the environment of the residential side open electricity market based on block chain technology. According to the needs of power trading, the power trading using block chain technology is divided into 7 steps and abstracted separately. Finally, the power transaction based on block chain technology is analyzed, and the transaction process is further explained in combination with the case, which provides a reference for the introduction of new technologies for the design of ubiquitous power internet of things power transactions.

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

An information provision system according to residents’ indoor comfort preferences for energy conservation

A Home Energy Management System is a key component to achieve energy efficiency on the residential side in a Smart City. The proposed system includes two processes: one to collect environmental and physical data using networked sensors, and the other to submit web-based questionnaires for calculating Predicted Mean Vote values, which is an indicator of personal comfort in homes. Another part of this system selects information obtained from the collected data every 30 min to encourage appropriate changes in personal behavior, so as to provide the preferred level of comfort. Information is generated for each resident’s comfort range. To test its applicability, the system was installed in two test households for two weeks in summer and in winter. In the first week, environmental and physical data were collected. This created a benchmark for electricity consumption. In the second week, information was provided, which was used to customize the comfort level for each person. The evaluation showed that the system contributed to a reduction in electricity consumption by approximately 13.3 %, and an increase in indoor comfort by approximately 65.5 % during both summer and winter, when electricity consumption peaks and indoor comfort is at its lowest.

Direct drive wave energy array with offshore energy storage supplying off‐grid residential load

Current developments in wave energy conversion have focused on locations where the wave energy resource is the highest; using large devices to generate hundreds of kilowatts of power. However, it is possible to generate power from low power waves using smaller wave energy devices. These lower rated wave energy converters can form arrays to supply power to remote coastal or island communities which are off-grid. This study introduces wave-to-wire modelling of wave energy arrays for off-grid systems using low power permanent-magnet linear generators (PMLGs). Offshore energy storage at the DC link is added to keep the voltage constant along with a current controller for the inverter in order to supply constant low harmonic power to the residential load connected off-grid. Simulation results produced in MATLAB/Simulink environment show that the wave energy array can generate power independently from the residential side by keeping the system stable using offshore storage. In addition, two different types of controllers for wave energy devices that use PMLGs are compared based on the power captured from the waves.

Cost Parameters of Green Residential Buildings in Pune, India

Various studies have been performed on cost impacts and impact parameters in Green Buildings against the conventional ones. Most of the studies include commercial buildings prioritizing energy and water related impacts while fewer studies are done on residential scale. This paper attempts to study the residential side of Green.

The Role of the Residence: Exploring the Goals of an Aboriginal Residential Program in Contributing to the Education and Development of Remote Students

Recent media and policy focus in remote Aboriginal education has turned to boarding schools. The general rhetoric is that boarding schools will allow Indigenous Australian students to have access to quality education and to learn to ‘walk in two worlds’. However, to date, there has been very little exploration of the lived experiences of Indigenous boarding schools, either from broader political and sociological perspectives, or from the schools themselves. Furthermore, understanding of how the residential side of boarding constructs the use of time and presents educational and social development opportunities is lacking. This paper aims to begin to address this, by presenting the goals and intended outcomes of a residential program for remote central Australian Aboriginal students. Through analysis of 17 semistructured interviews with residence staff, this paper identifies the two overarching goals of the program, as well as the more specific learning outcomes from which the program expects its students to benefit. The research presented is preliminary data that forms part of a broader PhD study of postboarding school expectations and outcomes for remote Aboriginal students, their families, and their communities.

Identification and Estimation for Electric Water Heaters in Direct Load Control Programs

PowerShift-Atlantic (PSA) is a pilot project lead by Canadian Maritime utilities that demonstrates direct load control strategies for up to 20 MW of commercial and residential loads for the purpose of balancing the intermittency of renewable generation and supporting demand-side management programs. On the residential side, domestic electric water heaters (DEWHs) form a significant end use class. The ability to accurately estimate and predict the state of individual end use devices allows aggregated control systems to better ensure end-use performance and comfort levels. This paper presents a methodology for estimating and predicting the state of individual DEWHs from models of their thermodynamics and water consumption that are derived under two scenarios: 1) when measurements of both power consumption and water temperature are available; and 2) when only measurements of power consumption are available. The proposed methodology was implemented as part of the PSA pilot project for the DEWH load class to simulate the behavior of the load in presence of the controller and evaluate the performance of the controller. Experimental results show that the model and water usage profile mimic the actual behavior of DEWHs, and can predict the future power consumption when the thermostatic control of a DEWH is interrupted as part of a load control strategy.

The Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) field study methodology

The Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) field study examined indoor and outdoor exposure to traffic-generated air pollution by studying the individual processes of generation of traffic emissions, transport and dispersion of air contaminants along a roadway, and infiltration of the contaminants into a residence. Real-time instrumentation was used to obtain highly resolved time-series concentration profiles for a number of air pollutants. The B-TRAPPED field study was conducted in the residential Sunset Park neighborhood of Brooklyn, NY, USA, in May 2005. The neighborhood contained the Gowanus Expressway (Interstate 278), a major arterial road (4(th) Avenue), and residential side streets running perpendicular to the Gowanus Expressway and 4(th) Avenue. Synchronized measurements were obtained inside a test house, just outside the test house façade, and along the urban residential street canyon on which the house was located. A trailer containing Federal Reference Method (FRM) and real-time monitors was located next to the Gowanus Expressway to assess the source. Ultrafine particulate matter (PM), PM(2.5), nitrogen oxides (NO(x)), sulfur dioxide (SO(2)), carbon monoxide (CO), carbon dioxide (CO(2)), temperature, relative humidity, and wind speed and direction were monitored. Different sampling schemes were devised to focus on dispersion along the street canyon or infiltration into the test house. Results were obtained for ultrafine PM, PM(2.5), criteria gases, and wind conditions from sampling schemes focused on street canyon dispersion and infiltration. For comparison, the ultrafine PM and PM(2.5) results were compared with an existing data set from the Los Angeles area, and the criteria gas data were compared with measurements from a Vancouver epidemiologic study. Measured ultrafine PM and PM(2.5) concentration levels along the residential urban street canyon and at the test house façade in Sunset Park were demonstrated to be comparable to traffic levels at an arterial road and slightly higher than those in a residential area of Los Angeles. Indoor ultrafine PM levels were roughly 3-10 times lower than outdoor levels, depending on the monitor location. CO, NO(2), and SO(2) levels were shown to be similar to values that produced increased risk of chronic obstructive pulmonary disease hospitalizations in the Vancouver studies.

The wireless local loop

In telephony, the loop is defined as the circuit connecting a subscriber's station (e.g., telephone set) with the line terminating equipment in a central office (a switch in the telephone network). The trunks start from the central office in the loop; they break down into several smaller bundles of circuits after moving away from the central office. These circuits are eventually separated into individual drops for the residential homes. The cost of the loop tends to be dominated by the residential side. (It is typically referred to as the "expensive last km"). This statement is particularly true for rural areas. The International Telecommunications Union's (ITU) projection indicates that 150 million new telephone lines will need to be installed in developing countries by the year 2000. As a result, telecommunication operators are looking to wireless technology to replace part of the hard-wire infrastructure. Wireless local loop (WLL) technology is being considered because the radio systems can be rapidly developed, easily extended and are distance insensitive. The WLL eliminates the wires, poles and ducts essential for a wired network.