Electrical Load Forecasting Using Edge Computing and Federated Learning

Taïk, Afaf; Cherkaoui, Soumaya

doi:10.1109/icc40277.2020.9148937

Cited by 165 publications

(82 citation statements)

References 25 publications

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“…In this way, user information such as energy preference and home addresses is not revealed to the cloud for privacy protection. Moreover, by cooperating data centers over different urban areas, the prediction model can achieve a high learning performance with better accuracy rate, compared to centralized learning solutions at a single server [158]. Another FL algorithm is also designed in [159] for electricity power learning in power IoT networks consisting of electric providers and IoT users.…”

Section: Fl For Smart Citymentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

698

182

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Section: Fl For Smart Citymentioning

confidence: 99%

Federated Learning for Internet of Things: A Comprehensive Survey

Nguyen

Ding

Pathirana

et al. 2021

IEEE Commun. Surv. Tutorials

698

182

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“…A regression approach is also followed by [125,126] who use electricity consumption in addition to environmental data for load forecasting using deep learning methods (DNN and a combination of Autoencoders and RNNs (GRU)). Edge based systems have been suggested by the authors of [127][128][129] for load forecasting for household consumers, Ref [129] use federated learning to train a RNN. In addition to load forecasting, smart grid management/monitoring is also a necessary application in this domain.…”

Section: Smart Energymentioning

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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“…Only one very recent work can be found in literature that, similarly to this paper, adopts Federated Learning for load forecasting at the edge [54]. As that work, we also rely on LSTM and envision the possibility to distribute the training burden among multiple edge nodes.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Savi

Olivadese

2021

IEEE Access

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Residential short-term energy consumption forecasting plays an essential role in modern decentralized power systems. The rise of innovative prediction methods able to handle the high volatility of users' electrical load has posed the basis to accomplish this task. However these methods, which mostly rely on Artificial Neural Networks, require that a huge amount of users' fine-grained sensitive consumption data are centrally collected to train a generalized forecasting model, with implications on privacy and scalability. This paper proposes an innovative architecture specifically designed to overcome this need. By exploiting Federated Learning and Edge Computing capabilities, many Long Short-Term Memory (LSTM) models are locally trained by different users based on their own historical energy consumption samples. Such models are then aggregated by a specific-purpose node to build a generalized model that is re-distributed for improved forecasting at the edge. For better forecasting, our proposed local training procedure takes as input relevant features related to calendar (i.e., hour, weekday and average consumption of previous days) and weather conditions (i.e., clustered apparent temperature), and the architecture can group users according to consumption similarities (using K-means) or socioeconomic affinities. We thoroughly evaluate the approach through simulations, showing that it can lead to similar forecasting performance than a stateof-the-art centralized solution in terms of Root Mean Square Error (RMSE), but with up to an order of magnitude lower training time and up to 50 times less exchanged data when samples are recorded at finer granularity than one hour. Nonetheless, it keeps sensitive data local and therefore guarantees users' privacy.

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Electrical Load Forecasting Using Edge Computing and Federated Learning

Cited by 165 publications

References 25 publications

Federated Learning for Internet of Things: A Comprehensive Survey

Federated Learning for Internet of Things: A Comprehensive Survey

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

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

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