Total utility demand prediction for multi-dwelling sites by a bottom-up approach considering variations of inhabitants’ behaviour schedules

Tanimoto, Jun; Hagishima, Aya; Iwai, Takeshi; Ikegaya, Naoki

doi:10.1080/19401493.2012.680498

Cited by 11 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ToU data sets focused on one-day diaries documenting the everyday activities of households of 5,000-10,000 households are common data sets used to build device use models (Wilke, 2013;Tanimoto et al, 2013;Richardson et al, 2010;Widén et al, 2012). Also, IJESM 16,4 there are long-term testing trials in which appliances have been monitored using electrical power measurements for extended periods (Page, 2007;Tabak and de Vries, 2010).…”

Section: Electricity Demand Models and Household Appliancementioning

confidence: 99%

The role of demand response in residential electricity load reduction using appliance shifting techniques

Avordeh

Gyamfi

Opoku

2021

IJESM

View full text Add to dashboard Cite

Purpose Some of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa. Design/methodology/approach The research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior. Findings This study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions. Research limitations/implications It should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households. Practical/implications Load shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers. Originality/value This paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

show abstract

Section: Electricity Demand Models and Household Appliancementioning

confidence: 99%

The role of demand response in residential electricity load reduction using appliance shifting techniques

Avordeh

Gyamfi

Opoku

2021

IJESM

View full text Add to dashboard Cite

show abstract

“…In response to these concerns, researchers have been developing a growing number of increasingly complex models (for example, stochastic, agent-based models), mostly derived by actual data (e.g. Haldi and Robinson 2010;Tanimoto et al 2013;Yun and Steemers 2008;Page et al 2008;Tanimoto and Hagishima 2005). Due to their conceptual differences, a distinction is made between the modelling formalisms used for presence, adaptive actions (actions performed as a reaction to indoor/outdoor variables, such as opening a window) and nonadaptive actions (such as the use of equipment).…”

Section: Occupant Behaviour and Modelling Complexitiesmentioning

confidence: 99%

A stepwise approach for assessing the appropriate occupant behaviour modelling in building performance simulation

Gaetani

Hoes

Hensen

2020

Journal of Building Performance Simulation

View full text Add to dashboard Cite

Occupant behaviour (OB) is recognized as a leading source of uncertainty in building performance predictions. Neglecting the potential influence of uncertainties on building performance could result in erroneous decision-making during the design phase. Therefore, it is essential that uncertainties are appropriately considered within building performance simulation (BPS) models. As for OB, there are various approaches to model occupant presence and actions in BPS tools. Literature shows that the appropriate modelling approach depends on the object and purpose of the simulation, which makes it difficult to favour a method over another a priori. Moreover, there is very little support for selecting the most appropriate modelling approach. As a result, OB is modelled in practice in various ways, mostly dictated by intuition and habit. This study builds on previous literature to introduce and test a complete approach for appropriate OB modelling. The method can be generalized and readily applied to design questions.

show abstract

“…The National Calculation Methodology (NCM) used in the UK for calculations of compliance to Building Regulations (Building Research Establishment 2015) is one class of bottom-up model based on space-use type. Other bottom-up models focus on individual device types (Menezes 2013;Rysanek and Choudhary 2015) or individual occupant behaviours (Tanimoto et al 2013). For nondomestic buildings as considered here, evidence suggests that the action of an individual occupant or individual device has less impact on the energy demand than the nature of the space use and hence a bottom-up model akin to the NCM approach would be the most suitable approach (Gilani, O'Brien, and Gunay 2018;Ward et al 2016a).…”

Section: Introductionmentioning

confidence: 99%

A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

Ward

Choudhary

Heo

et al. 2019

Journal of Building Performance Simulation

View full text Add to dashboard Cite

There is currently no established methodology for the generation of synthetic stochastic internal load profiles for input into building energy simulation. In this paper, a Functional Data Analysis approach is used to propose a new data-centric bottomup model of plug loads based on hourly data monitored at a high spatial resolution and by space-use type for a case-study building. The model comprises a set of fundamental Principal Components (PCs) that describe the structure of all data samples in terms of amplitude and phase. Scores (or weightings) for each daily demand profile express the contribution of each PC to the demand. Together the principal components and the scores constitute a structure-based model potentially applicable beyond the building considered. The results show good agreement between samples generated using the model and monitored data for key parameters of interest including the timing of the daily peak demand.

show abstract

Total utility demand prediction for multi-dwelling sites by a bottom-up approach considering variations of inhabitants’ behaviour schedules

Cited by 11 publications

References 14 publications

The role of demand response in residential electricity load reduction using appliance shifting techniques

The role of demand response in residential electricity load reduction using appliance shifting techniques

A stepwise approach for assessing the appropriate occupant behaviour modelling in building performance simulation

A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

Contact Info

Product

Resources

About