Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

Ijaz Ul Haq, Amin Ullah, Samee Ullah Khan, Noman Khan, Mi Young Lee, Seungmin Rho and Sung Wook Baik
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Ijaz Ul Haq: Sejong University, Seoul 143-747, Korea
Amin Ullah: Sejong University, Seoul 143-747, Korea
Samee Ullah Khan: Sejong University, Seoul 143-747, Korea
Noman Khan: Sejong University, Seoul 143-747, Korea
Mi Young Lee: Sejong University, Seoul 143-747, Korea
Seungmin Rho: Department of Industrial Security, Chung-Ang University, Seoul 156-756, Korea
Sung Wook Baik: Sejong University, Seoul 143-747, Korea

Mathematics, 2021, vol. 9, issue 6, 1-17

Abstract: The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the residential and industrial sectors use metering sensors that only measure the consumed energy but are unable to manage electricity. In this paper, we present a comparative analysis of a variety of deep features with several sequential learning models to select the optimized hybrid architecture for energy consumption prediction. The best results are achieved using convolutional long short-term memory (ConvLSTM) integrated with bidirectional long short-term memory (BiLSTM). The ConvLSTM initially extracts features from the input data to produce encoded sequences that are decoded by BiLSTM and then proceeds with a final dense layer for energy consumption prediction. The overall framework consists of preprocessing raw data, extracting features, training the sequential model, and then evaluating it. The proposed energy consumption prediction model outperforms existing models over publicly available datasets, including Household and Korean commercial building datasets.

Keywords: prediction model; sequential learning model; energy consumption; convolutional LSTM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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