Article
Multidisciplinary Sciences
Marc Wenninger, Andreas Maier, Jochen Schmidt
Summary: The study introduces a domestic electricity demand dataset in Germany, documenting the usage of 50 appliances in 15 households over a period of up to 3.5 years, which can be utilized for load-shifting and smart grid management.
Article
Engineering, Electrical & Electronic
Jiexiang Wu, Li Li, Jiangfeng Zhang
Summary: Residential demand response (DR) programs aim to reduce peak load from residential homes, but it is challenging to determine the maximum load reduction due to difficulties in modeling customer willingness to participate and diverse household loads. This paper presents an electricity cost minimization model that incorporates DR incentives and customer responsiveness to identify the realistic maximum demand flexibility.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Economics
Amin Karimu, Chandra Kiran B.Krishnamurthy, Mattias Vesterberg
Summary: This study investigates the impact of switching to a mandatory dynamic pricing scheme on welfare and emissions using Swedish household data. The findings suggest that there are relatively small changes in load patterns, welfare, and carbon emissions after implementing hourly retail pricing.
Review
Energy & Fuels
Amit Shewale, Anil Mokhade, Nitesh Funde, Neeraj Dhanraj Bokde
Summary: The residential sector plays a significant role in global energy demand and is expected to have a substantial increase in energy consumption. Demand response solutions, particularly home energy management systems (HEMSs), are considered effective ways to meet the growing energy demands while optimizing energy consumption and consumer comfort. However, the performance analysis of these techniques is limited.
Article
Construction & Building Technology
Diana Bogin, Meidad Kissinger, Evyatar Erell
Summary: This study compares two clustering approaches in segmenting population by lifestyle, finding that household characteristics method is more effective than electricity use behavior method in classifying households into sub-groups of similar size with significantly distinct annual electricity consumption.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Mitchell Curtis, S. T. Smith, Jacopo Torriti
Summary: DSR's slow penetration in electricity markets is mainly due to the difficulty in assessing flexibility potential. A new DSR estimation method using detailed profile information has been developed in this study, showing better error balance compared to previous methods.
ENERGY AND BUILDINGS
(2021)
Article
Business
Cristian Huse, Claudio Lucinda, Andre Ribeiro
Summary: This study examines the impact of a large temporary energy-savings program on the valuation of energy efficiency by Brazilian households and the counterfactual energy savings. The program only increases the valuations of households facing incentives in the form of an energy consumption quota and the effects of the program on valuations are temporary, reverting to prior levels after the program ends. The findings also show significant counterfactual energy savings related to the purchase of new refrigerators.
JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT
(2021)
Article
Multidisciplinary Sciences
Martin Pullinger, Jonathan Kilgour, Nigel Goddard, Niklas Berliner, Lynda Webb, Myroslava Dzikovska, Heather Lovell, Janek Mann, Charles Sutton, Janette Webb, Mingjun Zhong
Summary: The IDEAL household energy dataset consists of electricity, gas, and contextual data from 255 UK homes over a 23-month period, which can be used for various applications such as investigating energy demand patterns and building performance modeling.
Article
Multidisciplinary Sciences
Amit Shewale, Anil Mokhade, Amruta Lipare, Neeraj Dhanraj Bokde
Summary: The evolution of the smart grid has enabled residential users to efficiently manage their growing energy demand. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. In this paper, two novel home energy management models are proposed to optimize energy consumption, and price-based demand response techniques are incorporated. The proposed algorithms are shown to be efficient methods for home energy management, achieving significant cost reductions and peak-to-average ratio reductions.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Construction & Building Technology
Farinaz Falaki, Abdelatif Merabtine, Denis Martouzet
Summary: The main variables determining electricity consumption in residential buildings include dwelling size, financial status, age of the reference person in the household, and household size and composition. Understanding these variables is crucial for forecasting electricity demand, making Demand Response and Demand-Side Management decisions.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Construction & Building Technology
Jason Mair, Kiti Suomalainen, David M. Eyers, Michael W. Jack
Summary: The study examines distributed battery energy storage as a potential solution for increasing electricity supply and demand variability. It finds that battery capacity requirements vary significantly based on different operational modes and show seasonal variations. Aggregating households can significantly reduce per-house battery requirements for load smoothing and peak shaving.
ENERGY AND BUILDINGS
(2021)
Article
Computer Science, Information Systems
R. Geetha, K. Ramyadevi, M. Balasubramanian
Summary: The prediction of electricity consumption is crucial for smart energy management, as it plays an important role in planning power generation and distribution systems and understanding customer lifestyles. However, existing forecasting models have shown subpar accuracy and require improvement.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Energy & Fuels
Chris Matthew, Catalina Spataru
Summary: Achieving emissions reduction targets requires improved energy efficiency. Hourly demand modelling can capture behavior profiles, technology types, weather factors, and building typologies. However, whole systems energy modelling is limited by data availability, especially for commercial and industrial demand. This study expands the use of time-use survey data to model commercial and industrial electricity-heating at an hourly and individual building level, providing a widely applicable method. The validation of the model shows good accuracy and variability, making it suitable for combining with a supply-side model.
Article
Thermodynamics
Xiaolei Liu, Zi Lin
Summary: This study investigated the impact of the 2019 Coronavirus disease on the UK electricity demand and developed a deep-learning predictive model for forecasting. Additionally, the effects of the pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.
Article
Green & Sustainable Science & Technology
M. J. Ritchie, J. Avenant, J. A. A. Engelbrecht, A. J. Rix, M. J. Booysen
Summary: Solar PV systems are being discussed globally due to their benefits of slowing down fossil fuel depletion, providing sustainable and clean energy, and powering communities without access to electricity. However, the adoption of domestic rooftop solar in South Africa has been limited, partially due to uncertainties in the cost and benefit of large-scale battery-less installations. To address this uncertainty, simulation can be used to predict PV system design. We present a data-driven model and synthesizer for South African households, testing its suitability for the design and sizing of fixed-axis rooftop PV systems through a case study. The findings suggest that using simulated days resulted in slightly under-designed PV systems compared to using measured days, but a roundup strategy can ensure the correct number of PV modules for most systems tested. Additionally, the economic viability of the sized PV system in South Africa is determined based on the chosen export tariff.
ENERGY FOR SUSTAINABLE DEVELOPMENT
(2023)
