An integrative study of home energy management for residential energy consumers

This paper studies how to develop and evaluate demand response strategies from the consumer's perspective through a computational experiment approach. The proposed approach includes a home energy consumption simulator, a demand response mechanism obtained through optimization, particle swam or heuristic method, and an integrative computing platform that combines the home energy simulator and MATLAB together for demand response development and evaluation. Several demand response strategies are developed and evaluated through the computational experiment technique. The paper investigates and compares characteristics of different demand response strategies and how they are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.

This paper investigates how to develop a real-time demand response strategy from the consumer's perspective through a computational experiment approach. Typical conventional demand response methods are reviewed and their shortcomings are analyzed. Then, a smart demand response mechanism is proposed for potential real-time demand response implementation. An integrative computing platform that combines the home energy simulator and MatLab together is used for demand response development and evaluation. The proposed smart demand response mechanism is adaptive to weather and seasonal changes, which makes the optimal real-time demand response method more effective. The real-time demand response strategy is compared with conventional demand response strategies through the computational experiment technique. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.

This paper focuses on developing an interdisciplinary mechanism that combines machine learning, optimization, and data structure design to build a demand response and home energy management system that can meet the needs of real-life conditions. The loads of major home appliances are divided into three categories: 1) containing fixed loads; 2) regulate-able loads; and 3) deferrable loads, based on which a decoupled demand response mechanism is proposed for optimal energy management of the three categories of loads. A learning-based demand response strategy is developed for regulateable loads with a special focus on home heating, ventilation, and air conditioning (HVACs). This paper presents how a learning system should be designed to learn the energy consumption model of HVACs, how to integrate the learning mechanism with optimization techniques to generate optimal demand response policies, and how a data structure should be designed to store and capture current home appliance behaviors properly. This paper investigates how the integrative and learning-based home energy management system behaves in a demand response framework. Case studies are conducted through an integrative simulation approach that combines a home energy simulator and MATLAB together for demand response evaluation.

ABSTRACTAttempting to increase energy efficiency and improve system load factors in an electricity distribution system, Demand Response (DR) has long been proposed and implemented as a form of load management. Various pricing structures incentivizing consumers to shift energy consumption from on-peak to off-peak periods are evident in this field. Most DR methods currently used in practice belong to static variable pricing (e.g., Time of Use, Critical Peak Pricing) and the impact of such tariffs has been well established. However, dynamic variable pricing in general is less studied and much less practiced in the field, due to the lack of understanding of consumer behavior in response to price uncertainty. In this article, we study a novel dynamic variable pricing scheme that uses the coincident demand charge to reduce load consumption during peak events. We employ a multi-attribute utility function and model predictive control to simulate consumer behavior of utility maximization in home energy consumption. We use a conditional Markov chain to model and predict the system peak. Effects of the proposed residential electricity rate based on coincident demand charge are compared with other pricing schemes through simulation validated with real-world residential load profiles. Finally, we extend the simulations to study the impact of integrating renewable solar production in a DR program.

A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

Home Energy Management System (HEMS) is a technology to reduce and manage home energy use. The feedback on energy consumption to energy users is known to be effective to reduce total energy use. A typical HEMS just shows the energy consumption of the whole home and home appliances. Users cannot figure out how efficient a home appliance is, compared to the others. So it is necessary to compare the energy usage of home appliances to that of the same kinds of home appliances. In this paper, we propose a green HEMS based on energy comparison.

Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users’ cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the best-performing DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework’s applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.

Demand Response (DR) refers to modifications to electricity usage by consumers, which are derived by changes in the price of electricity or incentive payments offered to induce lower electricity use at specific periods. DR is a useful tool for not only electricity market players on both the supply and demand sides, but also Independent System Operators (ISO). On the supply side, DR is of particular interest for wind power producers, where they can use DR to alleviate their production intermittency as well as real-time market price variations. In addition, electricity retailers on the demand side may use DR to procure part of their clients’ energy and consequently cope with pool market price fluctuations. Furthermore, ISOs are faced with new challenges as the integration of renewable resources increases, and therefore may seek DR as a reserve provider. Despite the clear understanding of DR benefits for the above market players, they have practically low involvement in DR programs. They instead prefer to buy DR products from a third-party company. Such a company is called a DR aggregator, which is responsible for carrying out DR programs on consumers and selling the outcome to purchasers. To this end, an appropriate DR framework is needed to provide mutually attractive DR deals between the aggregator and DR purchasers. This research aims at proposing a new DR framework through which DR is traded as a public good between a DR aggregator and a DR purchaser. Various bilateral DR contracts with unique features are proposed and formulated for this purpose. The proposed DR framework is then applied to an offering strategy by wind power producers. Two well-known markets, i.e. the Australian National Electricity Market (NEM) and the Nordic market, are studied and proper wind offering plans in these markets are formulated. In addition, the behaviour of DR aggregators in power offering by a wind power producer is modelled. A bilevel problem is formulated in which the upper level refers to the wind power producer and the lower level models the DR aggregator behaviour. Furthermore, DR application by a strategic wind power producer, being able to alter market prices, is evaluated. To this end, a bilevel model is formulated in which the leader is the strategic wind power producer and followers are the market clearing mechanism and DR aggregator behaviour, respectively. The proposed DR framework is also applied to an energy procurement problem of electricity retailers. A cost minimization problem is modelled through which a retailer can purchase DR in addition to the commonly-used pool market and forward contracts. Lastly, the application of DR in an electricity market integrating high penetration of wind and PV resources is studied. A market dispatch is formulated in which an ISO allows DR aggregators to participate in the reserve market in order to cope with renewable power production uncertainty. The above problems are stochastically formulated to address the uncertainty of market prices as well as wind and PV power production. In addition, risk modelling is carried out using Conditional Value at Risk (CVaR). Each problem is rendered as a linear programming approach to be solved using General Algebraic Modelling System (GAMS), which is a commercially available optimization tool.

Demand response (DR) has been broadly recognized to be an integral component of well-functioning electricity markets, although currently underdeveloped in most regions. Among the various initiatives undertaken to remedy this deficiency, public utility commissions (PUC) and utilities have considered implementing dynamic pricing tariffs, such as real-time pricing (RTP), and other retail pricing mechanisms that communicate an incentive for electricity consumers to reduce their usage during periods of high generation supply costs or system reliability contingencies. Efforts to introduce DR into retail electricity markets confront a range of basic policy issues. First, a fundamental issue in any market context is how to organize the process for developing and implementing DR mechanisms in a manner that facilitates productive participation by affected stakeholder groups. Second, in regions with retail choice, policymakers and stakeholders face the threshold question of whether it is appropriate for utilities to offer a range of dynamic pricing tariffs and DR programs, or just ''plain vanilla'' default service. Although positions on this issue may be based primarily on principle, two empirical questions may have some bearing--namely, what level of price response can be expected through the competitive retail market, and whether establishing RTP as the default service is likely to result in an appreciable level of DR? Third, if utilities are to have a direct role in developing DR, what types of retail pricing mechanisms are most appropriate and likely to have the desired policy impact (e.g., RTP, other dynamic pricing options, DR programs, or some combination)? Given a decision to develop utility RTP tariffs, three basic implementation issues require attention. First, should it be a default or optional tariff, and for which customer classes? Second, what types of tariff design is most appropriate, given prevailing policy objectives, wholesale market structure, ratemaking practices and standards, and customer preferences? Third, if a primary goal for RTP implementation is to induce DR, what types of supplemental activities are warranted to support customer participation and price response (e.g., interval metering deployment, customer education, and technical assistance)?

A new generation of electric appliances with controllable reactive power creates an opportunity for operators in distribution systems to be used as a resource for reactive power support. On the other hand, implementing demand response (DR) programmes as an alternative resource to manage the active power demand may also affect the reactive power balance. Collaborative effect of reactive power support devices and the DR programme is investigated in order to control voltage problem in a distribution network. In the first step, distributed voltage (DV) control and DR methods are considered as individual control actions; then, the hybrid DV control with DR (DVDR) method is proposed to improve the voltage profile. The IEEE 33‐bus distribution standard test system is chosen for validating the novel method, in which the optimum reactive power injection in the candidate buses and demand curtailment in each area are calculated. The proposed DVDR method can better mitigate the voltage problem and the results showed far desirable performance compared with using just DR or DV methods. The proposed method can also curtail less demand in comparison to the DR method.

This paper deals with livelihood monitoring and care assessment of old age people based on internet of things. The usage of electrical and non-electrical home appliances as well as health conditions are monitored by the sensors such as electrical sensor, force sensor, temperature sensor and contact sensor. All sensors are integrated into a common sensor node and sensor data are analyzed to predict wellness parameters. Based on the wellness parameters, the daily activities of elderly people are classified as neither normal nor abnormal. In case of abnormal condition, the automatic alert will be given to nearby hospitals and local care takers for immediate medical assistance. Additionally, the well-being conditions are periodically updated in IoT cloud for real time monitoring. Experiment was conducted and the performance was tested by the prediction of well-being conditions based on usage and non-usage of home appliances. From results, it is inferred that the proposed approach suffices old-age community to lead safe life without fear.

A generic internet of things architecture for controlling electrical energy consumption in smart homes

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.

The discrete-time second-best dynamic road pricing scheme

In Japan, the electricity consumption of household domestic appliances is increasing. The introduction of the demand response (DR) framework will promote electricity consumption reductions in the household sector by limiting electricity usage and by regulating the price of electricity. Because the appropriate operation patterns differ for each user under the DR programme, a home energy management system (HEMS) will play an important role by considering the priority of various home appliances and by appropriately modifying appliance operations to minimize the impact on a user’s lifestyle. In this study, HEMS based on the Bayesian network is proposed; the system will learn the behaviour of a user and prioritize the operation of home appliances under restrictions on electricity consumption.