A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure

Konstantakopoulos, Ioannis C.; Barkan, Andrew R.; He, Shan; Veeravalli, Tanya; Liu, Huihan; Spanos, Costas J.

doi:10.1016/j.apenergy.2018.12.065

Cited by 99 publications

(39 citation statements)

References 44 publications

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“…More work providing energy intelligence to consumers has been performed by [166][167][168]. Konstantakopoulos et al [168] pose this as a regression problem, they propose a cloud-based system utilizing both ambient sensor data (lighting, temperature etc) and appliance power data to forecast resource usage for consumers using a RNN. They show a reduction in energy consumption for their users using this information.…”

Section: Smart Homesmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Homesmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…Different hidden layers participate in decision-making by using the feedback from the next layer that will feed back to the previous layer [25]. DL enables computers to perform complex calculations by 2 Complexity relying on simpler calculations to optimize computer efficiency [26]. It is difficult for a computer to understand complex data such as national or PV grids (under certain environments) or a series of data of a complex nature, so we use deep learning algorithms instead of usual learning methods [27].…”

Section: Deep Learningmentioning

confidence: 99%

Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter

Wang

2021

Complexity

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Traditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photovoltaic (PV) solar panels are used as an energy source because of free and environmental friendliness. However, this technology has become a source of inspiration for many researchers. The proposed method suggests to extract useful features from PV and wind generators and then train the system to control them and update the inputs according to prediction results. Solar energy produces energy that varies according to the external influences and the immediate changes in weather conditions. Solar panels are connected through an inverter with the grid, through which the work of the solar panels is monitored using the Internet. It is worth using neural networks (NN) to control variables and adopt system output of previous iteration in processing operations. Use of deep learning (DL) in the control of solar energy panels helps reduce the direct surveillance of the system online. Solar power generation systems mainly depend on reducing the pollution resulting from carbon emissions. Saving CO2 emission is the main purpose of PV panel cells, so smart monitoring can achieve better result in that case.

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“…Applying gamification for persuasive technologies to foster behavioral change activities have gained a lot of attention in recent years (Kappen and Orji 2017). Furthermore, promoting environmental sustainability practices taking from energy saving to pollution control has been the focus of many studies and researches as well (Akasiadis et al 2015;Tserstou et al 2017;Konstantakopoulos et al 2019). As an instance, (S17) provided a gamified interface to promote renewable energy usage by residential buildings (Akasiadis et al 2015).…”

Section: Behavioral Changementioning

confidence: 99%

“…From a different perspective, its application in human-system interaction is also notable, when used to motivate humans to interact with the system towards the benefit of the system. This approach has been used by Konstantakopoulos et al (2019), where a gamified framework is developed for smart building infrastructure to stimulate occupants to consider personal energy usage in order to be more environmentally friendly. Other applications are in global climate change (Nastis and Pagoni 2019), web and mobile applications (Zichermann and Cunningham 2011), to name a few.…”

Section: Introductionmentioning

confidence: 99%

Convergence of Gamification and Machine Learning: A Systematic Literature Review

Khakpour

Colomo‐Palacios

2020

Tech Know Learn

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Recent developments in human–computer interaction technologies raised the attention towards gamification techniques, that can be defined as using game elements in a non-gaming context. Furthermore, advancement in machine learning (ML) methods and its potential to enhance other technologies, resulted in the inception of a new era where ML and gamification are combined. This new direction thrilled us to conduct a systematic literature review in order to investigate the current literature in the field, to explore the convergence of these two technologies, highlighting their influence on one another, and the reported benefits and challenges. The results of the study reflect the various usage of this confluence, mainly in, learning and educational activities, personalizing gamification to the users, behavioral change efforts, adapting the gamification context and optimizing the gamification tasks. Adding to that, data collection for machine learning by gamification technology and teaching machine learning with the help of gamification were identified. Finally, we point out their benefits and challenges towards streamlining future research endeavors.

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A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure

Cited by 99 publications

References 44 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Deep Learning Based on Wireless Remote Sensing Model for Monitoring the Solar System Inverter

Convergence of Gamification and Machine Learning: A Systematic Literature Review

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