Load shapes obtained from smart meter data are commonly utilized to understand daily energy use patterns for adaptive operations in applications such as Demand Response (DR). However, they do not provide information on the underlying causes of specific energy use patterns – i.e., inference on appliances’ time-of-use (ToU) as actionable information. In this paper, we investigated a scalable machine learning framework to infer the appliances’ ToU from energy load shapes in a collection of residential buildings. A scalable and generalized inference model obviates the need for model training in each building to facilitate its adoption by relying on training data from a set of previously observed buildings with available appliance-level data. To this end, we demonstrated the feasibility of using load shape segmentation to boost ToU inference in buildings by learning from their nearest matches that share similar energy use patterns. To infer an appliance ToU for a building, classification models are trained for inference on subintervals of load shapes from matched buildings with known ToU. The framework was evaluated using real-world energy data from Pecan Street Dataport. The results for a case study on electric vehicles (EV) and dryers showed promising performance by using 15-min smart meter load shape data with 83% and 71% F-score values, respectively, and without in-situ training.
The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15–25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.
Research studies provided evidence on the energy efficiency of integrating personal thermal comfort profiles into the control loop of Heating, Ventilation, and Air-Conditioning (HVAC) systems (i.e., comfort-driven control). However, some conflicting cases with increased energy consumption were also reported. Addressing the limited and focused nature of those demonstrations, in this study, we have presented a comprehensive assessment of the energy efficiency implications of comfort-driven control to (i) understand the impact of a wide range of contextual factors and their combinatorial effect and (ii) identify the operational conditions that benefit from personal comfort integration. In doing so, we have proposed an agent-based modeling framework, coupled with EnergyPlus simulations. We considered five potentially influential parameters and their combinatorial arrangements including occupants’ thermal comfort characteristics, diverse multi-occupancy scenarios, number of occupants in thermal zones, control strategies, and climate. We identified the most influencing factor to be the variations across occupants’ thermal comfort characteristics - reflected in probabilistic models of personal thermal comfort - followed by the number of occupants that share a thermal zone, and the control strategy in driving the collective setpoint in a zone. In thermal zones, shared by fewer than six occupants, we observed potentials for average energy efficiency gain in a range between −3.5% and 21.4% from comfort-driven control. Accounting for a wide range of personal comfort profiles and number of occupants, the average (±standard deviation) energy savings for a single zone and multiple zones were in ranges of [−3.7 ± 4.8%, 5.3 ± 5.6%] and [−3.1 ± 4.9%, 9.1 ± 5.1%], respectively. Across all multi-occupancy scenarios, a range between 0.0% and 96.0% of combinations resulted in energy savings.
Demand response (DR) is considered an effective approach in mitigating the ever-growing concerns for supplying the electricity peak demand. Recent attempts have shown that the contribution from the aggregate impact of flexible individual residential loads can add flexibility to the power grid as ancillary services. However, current DR schemes do not systematically distinguish the varying potential of user contribution due to the highly-varied usage behaviors. Thus, this paper proposes a data-driven approach for quantifying the potential of individual flexible load users for participation in DR. We introduced a metric to capture the predictability of usage in a future DR event using the historical consumption data for different load types. The metric helps to sort the users with flexible loads in a community according to their potential for load shifting scenarios. We then evaluated the applicability of the metric in the DR context to assess the extent of energy reduction for different segments of users. In our analysis, we included electric vehicle, wet appliances (dryer, washing machine, dishwasher), and air conditioning. The analysis of real-world data shows that the approach is effective in identifying suitable user segments with higher predictive potential for demand reduction. We also presented a cross-appliance comparison for assessing the flexibility potential of different user segments. As a case study based on Pecan Street Project, the findings suggest that potentially ~160 MWh reduction might be achieved in Austin, TX through only 20% participation of the selected flexible loads for the residential sector during a 2-h event.
Power utilities rely on Demand Response (DR) programs in order to shave the peak load at critical times, when there is an excessive demand. In the context of automation, DR programs are categorized as manual or automated. With the emergence of home energy management (HEM) systems that monitor and operate the household appliances, the opportunities for automated DR have emerged. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers' intervention, given that many consumers might not engage enough to perform the manual DR. However, it has been shown that unjustified load compensation from many HEM-enabled consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target the consumers for participation based on certain criteria. To address this issue, in this paper, we proposed a method for the selection of consumers who are using smart appliances with the highest potential for a DR program. The proposed method measures the (1) frequency, (2) consistency, and (3) peak time usage of deferrable loads across several days. We evaluate our approach on a historical real-world electricity consumption dataset from residential households. The findings demonstrate the efficacy of the proposed method to sort consumers (with the smart appliance) based on their potential to participate in DR.
During peak hours, electrical loads of residential HVAC systems can cause imbalance between electricity supply and demand. Demand response (DR) control has the potential to facilitate supply-demand balance. The demand side load control can help reduce electricity consumption through DR programs. Since HVAC loads are the major contributors to peak loads, reducing HVAC load at peak hours is an objective of many demand response technologies. This paper analyzes performance of the dynamic thermostat controller (DTC) of HVAC systems in homes that have dynamic price of electricity. Based on a price signal, the DTC sets back the thermostat temperature to save electricity consumption and costs. The consumers choose participation in the DR program based on the real-time price of electricity and set a threshold price that the thermostat uses to set the temperature during peak power price period. Performance of DTC was analyzed for two cities, Austin TX and Chicago IL, and historical market price data were used to generate hourly based real-time price. In addition to different climate zones, two different types of residential buildings were modeled. Based on detailed energy simulation, the changes in HVAC system operation were analyzed when considering electric energy saving, peak power reduction, cost saving, as well as the potential thermal discomfort. The specific results show that dynamic pricing combined with DTC results in 12% annual energy cost savings for HVAC operation with the energy saving of up to 6%, but without significantly changing the thermal comfort.
Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge.
In the U.S., heating, ventilating and air conditioning (HVAC) systems are the largest consumers of electrical energy and a major contributor to peak demand. To reduce both peak load and energy cost, the set-point temperature of HVAC can be controlled depending on the electricity price. This paper presents a proposed controller that curtails peak load as well as saves electricity cost while maintaining reasonable thermal comfort. The controller changes set-point temperature when the retail price is higher than customers preset price. To evaluate the performance of the newly developed demand response controller, detailed energy models for two residential buildings are developed to analyze HVAC power consumption for different house sizes and floor plans. The house models are assumed to be located in Austin, Texas, USA and generated with OpenStudio and EnergyPlus. The design of internal load and occupation schedule are based on a residential energy consumption survey and experimental data by the Pecan Street Project, Austin, TX. In addition, historical data from Austin Energy for residential customers, 2012 is used to calibrate two house models. In addition, this paper uses the historical real-time wholesale price data for the Electricity Reliability Council of Texas (ERCOT) wholesale electricity market to model the two types of real-time tariffs that many utilities in the U.S. currently use to generate dynamic pricing for demand response programs. The simulation results show that our demand response controller could provide up to 10.8% of energy cost savings by using the proposed controller with dynamic pricing. While avoiding significant discomfort due to temperature change. Also, the results present potential for saving considering peak load by 24.7% and total electrical energy saving for HVAC in homes by 4.3% annually.
Occupant comfort is a dominant influence on the performance of HVAC operations. Most HVAC system operations rely on industry standards to ensure satisfactory environmental conditions during occupancy. Despite the increasing building energy consumption rates, occupants are not usually satisfied with indoor conditions in commercial buildings. To address this issue, in this paper, a framework for integrating personalized comfort preferences into HVAC control logic is introduced. As part of the framework, a user proxy, a comfort profile learning algorithm, and a building management system (BMS) controller are presented. The performance of the framework in a real building setting has been evaluated. The framework was successful in a small-scale experiment in increasing efficiency by improving user comfort and slight decrease in collective energy consumption.
Demand response and dynamic retail pricing of electricity are key factors in a smart grid to reduce peak loads and to increase the efficiency of the power grid. Air-conditioning and heating loads in residential buildings are major contributors to total electricity consumption. In hot climates, such as Austin, Texas, the electricity cooling load of buildings results in critical peak load during the on-peak period. Demand response (DR) is valuable to reduce both electricity loads and energy costs for end users in a residential building. This paper focuses on developing a control strategy for the HVACs to respond to real-time prices for peak load reduction. A proposed dynamic demand response controller (DDRC) changes the set-point temperature to control HVAC loads depending on electricity retail price published each 15 minutes and partially shifts some of this load away from the peak. The advantages of the proposed control strategy are that DDRC has a detailed scheduling function and compares the real-time retail price of electricity with a threshold price that customers set by their preference in order to control HVAC loads considering energy cost. In addition, a detailed single family house model is developed using OpenStudio and Energyplus considering the geometry of a residential building and geographical environment. This HVAC modeling provides simulation of a house. Comfort level is, moreover, reflected into the DDRC to minimize discomfort when DDRC changes the set-point temperature. Our proposed DDRC is implemented in MATLAB/SIMULINK and connected to the EnergyPlus model via building controls virtual test bed (BCVTB). The real-time retail price is based on the real-time wholesale price in the ERCOT market in Texas. The study shows that DDRC applied in residential HVAC systems could significantly reduce peak loads and electricity bills with a modest variation in thermal comfort.
Demand response to time-varying pricing of electricity is critical to a smart grid's efficient management of electrical resources. This paper presents a new approach to quantify residential demand responsiveness to (time-of-use) TOU rates, which does not entail an econometric estimation of TOU demand equations. Based on one of the four smart grid pilots in China, our approach uses the survey data collected in 2011 from 236 residents in Yinchuan to implement a Monte Carlo simulation to obtain the minimum, expected and maximum demand responsiveness to four TOU rate designs. We find that residents do not respond to TOU pricing when the TOU rate design only causes a 10% increase in their existing electricity bills under non-TOU rates. However, their estimated peak demand responsiveness is 8.41% (21.26%) when the peak-time price increases by 20% (40%). Based on these findings, we conclude that suitably designed TOU rates are useful to the efficient operation of a smart grid.
Centrally controlled heating, ventilation, and air conditioning (HVAC) systems in commercial buildings are operated by building management systems (BMS) based on the predefined operational settings and a set of assumptions. Despite the high rate of energy consumption by HVAC systems in commercial buildings, observations showed that a significant portion of the occupants remain dissatisfied with thermal conditions. One of the main reasons is that HVAC systems do not take into account personalized comfort preferences in their operational rules. This study proposes a framework to integrate building occupants in the HVAC control loop, learn their comfort profiles, and control the HVAC system based on occupants' personalized comfort profiles. The framework fuses occupants' comfort perception indices (i.e., comfort votes provided by users and mapped to a numerical value), collected through participatory sensing, and ambient temperature data, collected through a sensor network, and computes occupants' comfort profiles by using a fuzzy rule-based descriptive and predictive model. The performance of the comfort-profiling algorithm was assessed using human subject data and synthetically generated data. For actuation, a BMS controller was proposed and tested in two zones of an office building. The BMS controller uses a proportional controller algorithm that regulates room temperatures to be equidistant from preferred temperatures of all occupants in the same thermal zone. Validation of the framework components demonstrated that the nonlinear underlying pattern of the thermal comfort sensation scale could accurately be recognized. Results of the BMS controller experiments revealed that the proportional controller algorithm is capable of keeping the thermal zones' temperatures in the ranges of preferred temperatures.
A personalized measure for thermal comfort has been applied for use in combination with smart controls for building automation. Using data from a field study, we first show the superiority of personalized measures for thermal comfort compared to standard non-adaptive methods. Based on this knowledge we describe a methodology, using logistic regression techniques, to convert user votes to a probability of comfort. We also describe the interface used to collect the votes. We show that, for a given subject, our thermal profile converges against the probabilities found in the field study. As a case study we implemented the measure in a control algorithm to control the shading devices. The results clarify the mode of action and also show the effectiveness of the method.
The Global Challenge of Encouraging Sustainable Living