Mobile Apps Meet the Smart Energy Grid: A Survey on Consumer Engagement and Machine Learning Applications

Chadoulos, Spiros; Koutsopoulos, Iordanis; Polyzos, George C.

doi:10.1109/access.2020.3042758

Cited by 27 publications

(11 citation statements)

References 68 publications

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“…As any handbook will tell (Jannach, 2010 ; Ricci et al, 2016 ) RecSyss are meant to offer relevant information in myriad settings, basically dealing with situations where an abundance of information makes it hard to detect and select what is required in a specific situation, or with situations where information itself cannot be accessed. Recommender systems are now part of search engines (the PageRank algorithm), streaming providers (music, movies, podcasts), social networks (rankings in timelines, news feeds), webshops (suggesting similar products or services), platforms that mediate the gig economy (recommending short stays or car rides), and will no doubt feed into cyberphysical infrastructures [internet of things (Felfernig, 2019 ), smart cities (Quijano-Sánchez et al, 2020 ), connected cars, smart energy grids (Chadoulos et al, 2020 )]. On top of that behavioral advertising, which runs the business model of “free” services such as search engines and social networks, is a dedicated type of recommender system (Yun et al, 2020 ).…”

Section: Defining Recsyssmentioning

confidence: 99%

The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems

Hildebrandt

2022

Front. Artif. Intell.

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Recommendations are meant to increase sales or ad revenue, as these are the first priority of those who pay for them. As recommender systems match their recommendations with inferred preferences, we should not be surprised if the algorithm optimizes for lucrative preferences and thus co-produces the preferences they mine. This relates to the well-known problems of feedback loops, filter bubbles, and echo chambers. In this article, I discuss the implications of the fact that computing systems necessarily work with proxies when inferring recommendations and raise a number of questions about whether recommender systems actually do what they are claimed to do, while also analysing the often-perverse economic incentive structures that have a major impact on relevant design decisions. Finally, I will explain how the choice architectures for data controllers and providers of AI systems as foreseen in the EU's General Data Protection Regulation (GDPR), the proposed EU Digital Services Act (DSA) and the proposed EU AI Act will help to break through various vicious circles, by constraining how people may be targeted (GDPR, DSA) and by requiring documented evidence of the robustness, resilience, reliability, and the responsible design and deployment of high-risk recommender systems (AI Act).

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Section: Defining Recsyssmentioning

confidence: 99%

The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems

Hildebrandt

2022

Front. Artif. Intell.

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“…After classifying electrical appliances according to power in the background, through interesting prompts, users are reminded on the visualization page to reduce the use time of certain types of electrical appliances. For example, if your electricity consumption is higher than other 95% users this week, the system reminds you that you can reduce the use time of the air conditioner.Consumer incentive technology [13] motivates users to take specific activities in a gamified way. At the same time, when the user's power consumption is reduced, a pop-up window will inform the user of the reduction in power consumption this week, and a certain reward will be given to encourage users to make energy-saving behaviors.…”

Section: Energy Consumption Feedback Remindermentioning

confidence: 99%

Overview of Energy Visualization Technologies for Smart Home

Yawen¹,

Yi²,

Cao³

et al. 2022

Preprint

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“…Smart meters can also be placed at homes that share electricity usage information with the grid. IoSG can reduce electricity usage, thus facilitating the customers as well as improving the carbon footprints and conserving electricity resources [77][78][79][80][81][82].…”

Section: Internet Of Smart Gridsmentioning

confidence: 99%

Reliable Internet of Things: Challenges and Future Trends

et al. 2021

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The Internet of Things (IoT) is a vital component of many future industries. By intelligent integration of sensors, wireless communications, computing techniques, and data analytics, IoT can increase productivity and efficiency of industries. Reliability of data transmission is key to realize several applications offered by IoT. In this paper, we present an overview of future IoT applications, and their major communication requirements. We provide a brief survey of recent work in four major areas of reliable IoT including resource allocation, latency management, security, and reliability metrics. Finally, we highlight some of the important challenges for reliable IoT related to machine learning techniques, 6G communications and blockchain based security that need further investigation and discuss related future directions.

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Mobile Apps Meet the Smart Energy Grid: A Survey on Consumer Engagement and Machine Learning Applications

Cited by 27 publications

References 68 publications

The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems

The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems

Overview of Energy Visualization Technologies for Smart Home

Reliable Internet of Things: Challenges and Future Trends

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