Appliance level data analysis of summer demand reduction potential from residential air conditioner control

Malik, Anam; Haghdadi, Navid; MacGill, Iain; Ravishankar, Jayashri

doi:10.1016/j.apenergy.2018.11.010

Cited by 51 publications

(21 citation statements)

References 25 publications

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“…After the disaggregation, every appliance was bounded to a certain monitored activity. Other studies, such as the one presented in [142], made use of a smart meter, which periodically measured the global energy consumption in the house, combined with some smart plugs for punctually monitoring specific electrical devices. The goal was also to track elderly behaviour by detecting the usage of home appliances.…”

Section: Use Of Nilm In Aalmentioning

confidence: 99%

“…For this reason, HVAC equipment should not only be efficiently scheduled by the HEMS, but its real-time control during their periods of operation should be as efficient as possible. The authors of [142], in a study of a large appliance consumption database in Sydney, Australia, studied the real influence of air-conditioners on summer demand peaks. By clustering the load profiles and proposing load control strategies, they estimated that 9% of the total peak demand could be reduced.…”

Section: Guidelines For Future Researchmentioning

confidence: 99%

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NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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Section: Use Of Nilm In Aalmentioning

confidence: 99%

Section: Guidelines For Future Researchmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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“…In South Australia, ACs play a major role in rising of electricity peak demand where almost 90% of the residences are equipped with AC due to frequent heat waves [5]. During the summer season, residential ACs contribute about 20-25% in electrical system peak demand of the Ausgrid, a largest distribution utility in the New South Wales Sydney region of Australia [6].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN

Waseem

Lin

2019

BDCC

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Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.

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“…Under the influence of various factors like climate change, developing economy and consumption, residential air conditionings (RACs) have entered into a rapid development stage in China, while the number of RACs per 100 households has grown from 18.3 in 1997 to 128.6 in 2017. It is predicted that the penetration rate of RACs in China will go on increasing, compared with 281.7 in Japan in 2017.The substantial increase of RACs brings huge peak pressure to the system during summer and winter [1], [2]. In some big cities, the load proportion of RACs has reached 30%∼40% at summer, and in Shanghai, this number even exceeds 50%.…”

Section: Introductionmentioning

confidence: 99%

“…A PG&E pilot included nearly 2,000 residential households with control devices, that were instructed to cause an immediate and complete shutdown of the air conditionings compressor 71 times over a two and half month period in 2009 [9]. An appliance level consumption monitoring project proved the response potential of RACs account for 9% of total peak demand across the Australian State of New South Wales over 2012-2014 [2]. Since 2013, an ACs pilot has been carried out by Jiangsu Electric Power Company in China, with central ACs as main parts and de-centralized ACs as auxiliary parts, forming a data-base of 300MW load as flexible resources [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population

Chen

Yang

2019

IEEE Access

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Residential air conditionings (RACs) can be seen as one of the important flexible demand resources controlled to provide balance services for the power system due to their spatial thermal storage feature. However, such control on RACs will bring out the negative impact on the comfort level of residents inevitably, affecting their proactivity in demand response (DR). So, a novel two-stage control model of large-scale RACs is proposed to participate in DR considering different comfort sensitivity of various residents. The comfort sensitivity index is developed in terms of daily operation time and daily power consumption per area of RACs, classified by K-means clustering. Besides, the DR potential of RACs is established with the adjusted temperature setpoint, where the temperature setpoint is determined by the comfort sensitivity index. Moreover, a two-stage control model of large-scale RACs is proposed comprised of load response and recovery, where the corresponding control strategies at each stage are proposed. Simulations of the proposed method have been performed on residential 20,000 RACs to validate its effectiveness in Changzhou, China. The outcome reveals that the strategies could maximize the DR potential of RACs and ensure the stable recovery of RACs under the premise of meeting the comfort demand of differentiated population.

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Appliance level data analysis of summer demand reduction potential from residential air conditioner control

Cited by 51 publications

References 25 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN

A Two-Stage Control Strategy of Large-Scale Residential Air Conditionings Considering Comfort Sensitivity of Differentiated Population

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