A systems dynamics approach to the bottom-up simulation of residential appliance load

doi:10.1016/j.enbuild.2021.111164

Energy and Buildings

Volume 247, 15 September 2021, 111164

https://doi.org/10.1016/j.enbuild.2021.111164 Get rights and content

Highlights

•
First attempt at bottom-up demand-side System Dynamics model.
•
A synthetic residential load model which is not driven by historic probabilities.
•
A potentially simpler logic to generating synthetic residential load is presented.
•
Lower cost, simpler interpretability and potential for transdisciplinary research.
•
The model outputs distribution of appliances’ operation.

Abstract

Residential demand from real residences can be resource intensive to collect. There is need to generate synthetic residential load in energy research, and new approaches are welcome. Most of the simulation models of synthetic residential load that output realistic loads are tightly coupled to historic correlations. This paper presents a high-resolution simulation model that generates a residential appliance load using the tools of System Dynamics via a bottom-up approach. In addition to being realistic, the model aims to minimise historic coupling. Whilst the intermediary outputs of the modelling process are subjected to systematic scrutiny, the final output is validated by comparing statistical characteristics of the model’s output to a validated model and data from real residences. The aims of the model were sufficiently met, and the modelling approach shows potential to simplify; by driving the model on average frequency of appliance use instead of probability distributions of human activities. Other outputs from the model, specifically distribution of appliances’ activation and operation, as well as complexity are discussed. Some benefits of the model are also discussed especially with regard to cost of modelling, interpretability of model and potential for transdisciplinary research. This study represents the first attempt to develop a bottom-up simulation model of residential load based on a System Dynamics approach.

Introduction

With sustainability a core global agenda, this has afforded many research opportunities to reimagine residential and community energy systems for a sustainable future. Some of the research includes simulations of renewable energy systems, demand side management, smart grids, building simulation, and low voltage grid simulation, all which require residential load as input. However, it can be expensive and time consuming to measure residential loads for use in these simulations. The cheaper and faster alternative is to generate realistic residential loads synthetically via simulations, which provides an opportunity to explore new approaches to generating realistic synthetic load profiles. This paper presents the first attempt to generate synthetic residential load using a System Dynamics approach.

The paper begins by providing a background to behaviour and activities in residential energy use, followed by a deeper look at how residential activities are measured. System Dynamics is subsequently introduced, and existing models are reviewed. The methods section is divided into conceptual model and simulation model. Results are discussed in terms of validation, other model outputs, complexity and evaluation of the model’s aims. Finally, some conclusions are drawn and further work is discussed.

Section snippets

Energy use, behaviour and activities

There is agreement that occupant behaviour is a major determinant of residential energy consumption [1], [2], [3], [4], [5]. In addition to recognising that occupants are the primary consumers of energy, not buildings [4], occupant behaviour can undermine technological solutions to efficient energy use [2], [3]. Behaviour is also recognised as a leverage point in public policy to influence energy use [1], [4], [5]. Another approach is to focus on energy consuming activities in households as

Conceptual model and validity tests

This section looks into the conceptual model and addresses the concerns of validity tests raised in Table 1.

Load Profile

The load profiles for a 7-day period for the CREST model, SD model and a residence from UKDA dataset are shown in Fig. 16, Fig. 17 and Fig. 18 respectively; all have two residents. Fig. 18 is the residence with the same appliances as the CREST and SD models. The three load profiles highlight the similarities of varying and steep peaks resulting from different activities, as well as similar amplitudes between the CREST and SD models. The absence of these similarities would terminate further

Conclusion and further work

The tools of system dynamics have been utilised to simulate a residential load using a bottom-up approach, and the aims of the model have been achieved. The model was conceptualised as a CLD based on literature and reasonable assumptions, and from that, a simulation model was presented as a SFD. Both the conceptual and simulation models addressed the concerns of SD validity tests. The output load from the SD model was compared to output from the well validated CREST model, as well as load

Funding

This work was supported by the Petroleum Technology Development Fund (PTDF) of Nigeria.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References (45)

R.M. Tetlow et al.
Identifying behavioural predictors of small power electricity consumption in office buildings
Build. Environ.
(2015)
T. Hong et al.
Ten questions concerning occupant behavior in buildings: The big picture
Build. Environ.
(2017)
M.A. Ortiz et al.
Proof-of-concept of a questionnaire to understand occupants’ comfort and energy behaviours: First results on home occupant archetypes
Build. Environ.
(2018)
L. Nicholls et al.
Peak demand and the “family peak” period in Australia: Understanding practice (in)flexibility in households with children
Energy Res. Soc. Sci.
(2015)
T. Gärling et al.
Psychological Explanations of Participation in Everyday Activities
Adv. Psychol.
(1993)
P. Grunewald et al.
The electricity footprint of household activities - implications for demand models
Energy Build.
(2018)
S. Herkel et al.
Towards a model of user behaviour regarding the manual control of windows in office buildings
Build. Environ.
(2008)
C.F. Reinhart
Lightswitch-2002: A model for manual and automated control of electric lighting and blinds
Sol. Energy.
(2004)
Y. Zhang et al.
Factors influencing the occupants’ window opening behaviour in a naturally ventilated office building
Build. Environ.
(2012)
V. Fabi et al.
Verification of stochastic behavioural models of occupants’ interactions with windows in residential buildings
Build. Environ.
(2015)

Y. Zhang et al.

Factors influencing occupants’ blind-control behaviour in a naturally ventilated office building

Build. Environ.

(2012)

C. Kandler et al.

Modeling lighting as part of the USER model based on stochastic time budget survey data

Energy Procedia.

(2015)

T. Hong et al.

An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema

Build. Environ.

(2015)

J. Widén et al.

A high-resolution stochastic model of domestic activity patterns and electricity demand

Appl. Energy.

(2010)

I. Richardson et al.

Domestic electricity use: A high-resolution energy demand model

Energy Build.

(2010)

R. Bartels et al.

An end-use electricity load simulation model. Delmod, Util

Policy.

(1992)

R. Yao et al.

A method of formulating energy load profile for domestic buildings in the UK

Energy Build.

(2005)

J. Widén et al.

Constructing load profiles for household electricity and hot water from time-use data-Modelling approach and validation

Energy Build.

(2009)

N. Pflugradt et al.

Synthesizing residential load profiles using behavior simulation

Energy Procedia.

(2017)

C. Wilson et al.

Models of Decision Making and Residential Energy Use

Annu. Rev. Environ. Resour.

(2007)

E.R. Frederiks et al.

The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review

Energies.

(2015)

L. Nicholls, Y. Strengers, Changing demand: flexibility of energy practices in households with children,...

Cited by (3)

Non-intrusive residential electricity load decomposition via low-resource model transferring
2023, Journal of Building Engineering
Non-intrusive load decomposition (NILD) technology has a broad application prospect because it can deeply excavate the internal electricity consumption data of customers and obtain electricity consumption information. A non-intrusive load decomposition method based on model transfer is proposed to address the problem of insufficient accumulation of historical electricity consumption data of some household customers. Firstly, Time2vec, an embedding layer with time vectors, is introduced as an encoder to convert the time series into a continuous vector space, which improves the computational efficiency of the model for power time series information. Then, by introducing the Channel-wise attention mechanism, favorable residential electricity load characteristics can be effectively obtained. The model transfer method is used to fine-tune the pre-trained model based on complete data to overcome the problem of insufficient accumulation of historical electricity consumption data of some households. The experimental results show that the proposed method is a significant improvement over the existing state-of-the-art methods. The proposed method significantly improves the accuracy of load decomposition while reducing the model parameters. The model parameters are reduced by 62.4%, and the average absolute error is reduced by 13.8%–59.3%. Even with a small number of data training source domains, the proposed method is still able to accurately obtain the power consumption information of each appliance, proving that the proposed method overcomes the problem of insufficient accumulation of electricity consumption data of household users.
Convenience in a residence with demand response: A system dynamics simulation model
2022, Applied Energy
Citation Excerpt :
Briefly analyse money saved by different DR programs. The model (and its parameters) is based on a residence with three occupants, which is the 3-person residence from an earlier work [30]. The appliances are shown in Table 2, which shows the quantity of appliances, which appliances are on DR, dependency among the appliances, mean cycle duration, and cycles per day.
Demand Side Management (DSM) is a means to gain more control over energy demand to address some of the challenges of power grids. Demand Response (DR) is an approach to DSM that aims to influence the operation times of appliances; DR is often recommended for residences. Meanwhile, residents can undermine DR if it is not convenient. Therefore, there is need for tools to aid decision-making on the appropriate DR program for residences. Whilst models are used to explore DR programs, most do not measure, visualise or analyse the convenience of residents, although some models make assumptions about convenience. This paper explores convenience of a residence as timeliness by simulating four scenarios of DR programs in a single residence, using the System Dynamics (SD) methodology. In addition to delay in appliance-use that may result from DR, two indicators of convenience are proposed that consider preferences of the residence: Delay Duration Profile (DDP) and Delay Time Profile (DTP). When comparing convenience as delay, it was found that more hours of DR is better than less, earlier hours (from occupancy period) are better, and splitting or distributing DR hours during the day is better than being contiguous. Similar findings apply to DDP and DTP. Furthermore, it was found that DR leads to monetary savings and reduction in daily peak demand. This study represents the first attempt at a DR model from the bottom-up using SD, as well as using the model in decision-making analysis.
Uncertainty modeling of household appliance loads for smart energy management
2022, Energy Reports
Households represent a large share of flexibility on the demand side for assisting the smooth operation of the highly renewable grid. The stochastic nature of activities and various appliance usage patterns of household occupants affects the flexibility potential greatly, but is usually oversimplified in the existing research. This paper aims to propose a novel method for simulating the dynamics of household energy-related activities and appliance usage, which provides realistic synthetic load profiles for smart energy management. To this aim, a highly resolved multi-agent system model is proposed, which comprises an Agent-Based Activity Chain Model (ABACM) and 34 types of common household appliance models. Firstly, the patterns of various occupant behaviors are obtained through mining of real-world residential time-of-use data. Then, the stochastic activity profiles of occupants are generated using the ABACM, with a root mean square error of 0.95%. Finally, the electricity consumption profiles of household appliances are simulated based on specific energy-related activities. The proposed method is validated and proved to be able to capture stochastic occupant behaviors and represent the dynamics of residential energy consumption. The future work will focus on using the proposed model for exploring the potential of residential demand response.

View full text

A systems dynamics approach to the bottom-up simulation of residential appliance load

Highlights

Abstract

Introduction

Section snippets

Energy use, behaviour and activities

Conceptual model and validity tests

Load Profile

Conclusion and further work

Funding

Declaration of Competing Interest

Build. Environ.

Build. Environ.

Build. Environ.

Energy Res. Soc. Sci.

Adv. Psychol.

Energy Build.

Build. Environ.

Sol. Energy.

Build. Environ.

Build. Environ.

Build. Environ.

Energy Procedia.

Build. Environ.

Appl. Energy.

Energy Build.

Policy.

Energy Build.

Energy Build.

Energy Procedia.

Models of Decision Making and Residential Energy Use

Annu. Rev. Environ. Resour.

The socio-demographic and psychological predictors of residential energy consumption: A comprehensive review

Energies.