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DeepEnergy: Prediction of Appliances Energy with Long-Short Term Memory Recurrent Neural Network

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Intelligent Information and Database Systems (ACIIDS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10752))

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Abstract

Our world is becoming more interconnected and intelligent, huge amount of data has been generated newly. Home appliances’ energy usage is the basis of home energy management and highly depends on weather condition and environment. Using weather in context, it is theorized that usage of home energy would be higher in cold days. Time series and contextual data collected from sensors can be monitored and controlled in home appliances network. The aim of this work is to propose a deep neural network architecture and apply it to a contextual and multivariate time series data. Long short-term memory (LSTM) models are powerful neural networks based on past behaviours in long sequences. LSTM networks have been demonstrated to be particularly useful for learning sequences containing longer-term patterns of unknown length, due to their ability to maintain long-term memory. In this work, we incorporate contextual features into the LSTM model because of ability of keeping context of data for a long-time, and for analysing it we integrated two different datasets; the first dataset contains measurements about house temperature and humidity measured over a period of 4.5 months by a 10 min intervals using a ZigBee wireless sensor network. The second dataset contains measurements about individual household electric power consumptions gathered over a period of 47 months. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction.

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Acknowledgement

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826) in the Republic of Korea and also was supported by the National Natural Science Foundation of China (61702324) in People’s Republic of China.

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Authors and Affiliations

  1. Database and Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, South Korea

    Erdenebileg Batbaatar, Hyun Woo Park, Dingkun Li & Keun Ho Ryu

  2. College of Information Engineering, Shanghai Maritime University, 213, 1550 Haigang Avenue, Pudong New Area, Shanghai, People’s Republic of China

    Meijing Li

Authors
  1. Erdenebileg Batbaatar
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  2. Hyun Woo Park
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  3. Dingkun Li
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  4. Meijing Li
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  5. Keun Ho Ryu
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Corresponding author

Correspondence to Keun Ho Ryu .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Quang Binh University, Dong Hoi City, Vietnam

    Duong Hung Hoang

  3. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  4. Rutgers University, Piscataway, New Jersey, USA

    Hoang Pham

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Batbaatar, E., Park, H.W., Li, D., Li, M., Ryu, K.H. (2018). DeepEnergy: Prediction of Appliances Energy with Long-Short Term Memory Recurrent Neural Network. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-75420-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75419-2

  • Online ISBN: 978-3-319-75420-8

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