Residential electricity consumption and loads pattern analysis

Rosin, Argo; Hõimoja, Hardi; Möller, Taavi; Lehtla, Madis

doi:10.1109/pq.2010.5550009

Cited by 23 publications

(6 citation statements)

References 5 publications

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“…In a typical household, the load will be much higher when customers are active at home. The main consumption time of conventional EWH is during 7:00 a.m.-11:00 a.m. and 6:00 p.m.-12:00 a.m., i.e., when people usually have showers [26]. When performing EWH DSM, it is ideal to shift the EWH load from formal peak hours to valley hours to reduce the peaks.…”

Section: Win-win Situation For System Operator and Customermentioning

confidence: 99%

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Lin

Xiao

2017

Energies

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This paper investigates how to develop a learning-based demand response approach for electric water heater in a smart home that can minimize the energy cost of the water heater while meeting the comfort requirements of energy consumers. First, a learning-based, data-driven model of an electric water heater is developed by using a nonlinear autoregressive network with external input (NARX) using neural network. The model is updated daily so that it can more accurately capture the actual thermal dynamic characteristics of the water heater especially in real-life conditions. Then, an optimization problem, based on the NARX water heater model, is formulated to optimize energy management of the water heater in a day-ahead, dynamic electricity price framework. A genetic algorithm is proposed in order to solve the optimization problem more efficiently. MATLAB (R2016a) is used to evaluate the proposed learning-based demand response approach through a computational experiment strategy. The proposed approach is compared with conventional method for operation of an electric water heater. Cost saving and benefits of the proposed water heater energy management strategy are explored.

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Section: Win-win Situation For System Operator and Customermentioning

confidence: 99%

Optimal and Learning-Based Demand Response Mechanism for Electric Water Heater System

Lin

Xiao

2017

Energies

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“…Taking into account the load profile of the residential sector disaggregated by end-uses [14], it is possible to identify the typical usage schedule of different appliances, as well as estimating their contribution to the total electricity consumption and electricity bill (Figure 2). Information such as the regular time of usage of end-use loads is very useful to extract habits and the potential of using certain appliances in other schedules.…”

Section: Electricity Consumption In Portugalmentioning

confidence: 99%

“…Clothes dryers are normally used after the working cycle of clothes washing machines is completed. An important part of the consumption is not disaggregated by end-use loads since it was not possible to get the data from DGGE [14] and therefore equipments like air conditioners and electric heaters are not identifiable on this load profile. Other appliances commonly used at the residential sector, such as microwaves, nd European Energy Conference cooking stoves, hair dryers, and mobile phones chargers, although contributing for the aggregated consumption, are considered as uncontrollable loads.…”

Section: Electricity Consumption In Portugalmentioning

confidence: 99%

“…The potential of manageable demand can be identified in Figure 3 consisting of the sum of the loads that can be interrupted or have the thermostat parameters re-set plus loads that can be shifted. According to the categorization presented in Table 1 and the typical annual consumption of loads in Portugal [14], it is possible to develop a graph that shows the type of control that can be applied over the different end-use loads and their annual electricity consumption (Figure 4). However, despite the possibility of some loads being the target of the same type of control, the characteristics of the control actions (interruption duration, temperature parameters, time deferral, etc.)…”

Section: Manageable Demand and Load Categorizationmentioning

confidence: 99%

“…Automated demand response (ADR) could be a major help in dealing with this variability and thus contributing for the integration of renewables [1,3 6]. However, before establishing automated response actions, an adequate analysis of the disaggregated electricity consumption in the residential sector along with the typical patterns of load usage that can be somehow controlled is required [7]. Additionally, to assure the success of smart ADR actions to be implemented on manageable demand, technical constraints and consumer preferences that frame the way loads can be controlled must also be considered.…”

Section: Introductionmentioning

confidence: 99%

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Integrated Management of Residential Energy Resources

Soares

Gomes

Antunes

2012

EPJ Web of Conferences

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Abstract. The increasing deployment of distributed generation systems based on renewables in the residential sector, the development of information and communication technologies and the expected evolution of traditional power systems towards smart grids are inducing changes in the passive role of end-users, namely with stimuli to change residential demand patterns. The residential user should be able to make decisions and efficiently manage his energy resources by taking advantages from his flexibility in load usage with the aim to minimize the electricity bill without depreciating the quality of energy services provided. The aim of this paper is characterizing electricity consumption in the residential sector and categorizing the different loads according to their typical usage, working cycles, technical constraints and possible degree of control. This categorization of end-use loads contributes to ascertain the availability of controllable loads to be managed as well as the different direct management actions that can be implemented. The ability to implement different management actions over diverse end-use load will increase the responsiveness of demand and potentially raises the willingness of end-users to accept such activities. The impacts on the aggregated national demand of large-scale dissemination of management systems that would help the end-user to make decisions regarding electricity consumption are predicted using a simulator that generates the aggregated residential sector electricity consumption under variable prices.

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