ORCID Profile
0000-0003-3555-855X
Current Organisation
UNSW Sydney
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Publisher: Elsevier BV
Date: 11-2022
Publisher: MDPI AG
Date: 06-12-2022
DOI: 10.3390/EN15239257
Abstract: Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home’s energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes.
Publisher: Elsevier BV
Date: 11-2021
Publisher: EDP Sciences
Date: 2018
DOI: 10.1051/REES/2018003
Abstract: Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For ex le, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to in idual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast in idual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution 5, 15, 30 and 60 min, each resolution analysed for four different horizons 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.
Publisher: Elsevier BV
Date: 10-2019
No related grants have been discovered for Baran Yildiz.