Predicting residential energy consumption using CNN-LSTM neural networks

Energy - Tập 182 - Trang 72-81 - 2019
Tae Young Kim1, Sung-Bae Cho1
1Department of Computer Science, Yonsei University, Seoul, South Korea

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Tài liệu tham khảo

Zhao, 2017, Energy consumption in machining: classification, prediction, and reduction strategy, Energy, 133, 142, 10.1016/j.energy.2017.05.110

Sieminski, 2017, 5

Nejat, 2015, A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries), Renew Sustain Energy Rev, 43, 843, 10.1016/j.rser.2014.11.066

Ibrahim, 2008, Energy storage systems-characteristics and comparisons, Renew Sustain Energy Rev, 12, 1221, 10.1016/j.rser.2007.01.023

Deb, 2017, A review on time series forecasting techniques for building energy consumption, Renew Sustain Energy Rev, 74, 902, 10.1016/j.rser.2017.02.085

Kim, 2017, Modular Bayesian networks with low-power wearable sensors for recognizing eating activities, Sensors, 17, 1, 10.3390/s17122877

Arghira, 2012, Prediction of appliances energy use in smart homes, Energy, 48, 128, 10.1016/j.energy.2012.04.010

Ahmad, 2017, Seasonal decomposition of electricity consumption data, Review of Integrative Business and Economics Research, 6, 271

Chujai, 2013, Time series analysis of household electric consumption with ARIMA and ARMA models, Proc. of the Int. Multi-Conf. of Engineers and Computer Scientists, 1, 295

Hébrail, 2012

Koop, 1996, Impulse response analysis in nonlinear multivariate models, J Econom, 74, 119, 10.1016/0304-4076(95)01753-4

Beckel, 2014, Revealing household characteristics from smart meter data, Energy, 78, 397, 10.1016/j.energy.2014.10.025

Brown, 2017, Occupancy based household energy disaggregation using ultra wideband radar and electrical signature profiles, Energy Build, 141, 134, 10.1016/j.enbuild.2017.02.004

Beckel, 2014, Revealing household characteristics from smart meter data, Energy, 78, 397, 10.1016/j.energy.2014.10.025

Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans Pattern Anal Mach Intell, 35, 1798, 10.1109/TPAMI.2013.50

Donahue, 2015, Long-term recurrent convolutional networks for visual recognition and description, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2625

Greff, 2017, LSTM: a search space odyssey, IEEE Trans. on Neural Networks and Learning Systems, 28, 2222, 10.1109/TNNLS.2016.2582924

Wang, 2016, Dimensional sentiment analysis using a regional CNN-LSTM model, Proc. of the Annual Meeting of the Association for Computational Linguistics, 2, 225

Sainath, 2015, Convolutional, long short-term memory, fully connected deep neural networks, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 4580

Ullah, 2018, Action recognition in video sequences using deep bi-directional LSTM with CNN features, IEEE Access, 6, 1155, 10.1109/ACCESS.2017.2778011

Oh, 2018, Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats, Comput Biol Med, 102, 278, 10.1016/j.compbiomed.2018.06.002

Zhao, 2017, Learning to monitor machine health with convolutional bi-directional LSTM networks, Sensors, 17, 273, 10.3390/s17020273

Wang, 2012, Decomposition and statistical analysis for regional electricity demand forecasting, Energy, 41, 313, 10.1016/j.energy.2012.03.011

Kim, 2018, Predicting the household power consumption using CNN-LSTM hybrid networks, 481

Guo, 2018, A deep learning model for short-term power load and probability density forecasting, Energy, 160, 1186, 10.1016/j.energy.2018.07.090

Bouktif, 2018, Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches, Energies, 11, 1, 10.3390/en11071636

Fumo, 2015, Regression analysis for prediction of residential energy consumption, Renew Sustain Energy Rev, 47, 332, 10.1016/j.rser.2015.03.035

Amber, 2015, Electricity consumption forecasting models for administration buildings of the UK higher education sector, Energy Build, 90, 127, 10.1016/j.enbuild.2015.01.008

Vu, 2015, A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables, Appl Energy, 140, 385, 10.1016/j.apenergy.2014.12.011

Braun, 2014, Using regression analysis to predict the future energy consumption of a supermarket in the UK, Appl Energy, 130, 305, 10.1016/j.apenergy.2014.05.062

Chen, 2017, Short-term electrical load forecasting using the support vector regression (SVR) model to calculate the demand response baseline for office buildings, Appl Energy, 195, 659, 10.1016/j.apenergy.2017.03.034

Yaslan, 2017, Empirical mode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting, Measurement, 103, 52, 10.1016/j.measurement.2017.02.007

Bogomolov, 2016, Energy consumption prediction using people dynamics derived from cellular network data, EPJ Data Science, 5, 1, 10.1140/epjds/s13688-016-0075-3

Jain, 2014, Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy, Appl Energy, 123, 168, 10.1016/j.apenergy.2014.02.057

Kong, 2017, Short-term residential load forecasting based on LSTM recurrent neural network, IEEE Trans. on Smart Grid (Early Access), 1

Li, 2017, Building energy consumption prediction: an extreme deep learning approach, Energies, 10, 1525, 10.3390/en10101525

Shi, 2017, “Deep learning for household load forecasting–a novel pooling deep RNN, IEEE Trans. on Smart Grid, 9, 5271, 10.1109/TSG.2017.2686012

Marino, 2016, Building energy load forecasting using deep neural networks, IEEE Annual Conf. on Industrial Electronics Society, 7046

Ronao, 2016, Anomalous query access detection in RBAC-administered databases with random forest and PCA, Inf Sci, 369, 238, 10.1016/j.ins.2016.06.038

Ronao, 2017, Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models, Int J Distributed Sens Netw, 13, 1

Koschwitz, 2018, Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: a comparative study on district scale, Energy, 165, 134, 10.1016/j.energy.2018.09.068

Kim, 2018, Web traffic anomaly detection using C-LSTM neural networks, Expert Syst Appl, 106, 66, 10.1016/j.eswa.2018.04.004

Zhou, 2015

He, 2015, Convolutional neural networks at constrained time cost, IEEE Conf. on Computer Vision and Pattern Recognition, 5353

Kim, 2019, Electric energy consumption prediction by deep learning with state explainable autoencoder, Energies, 12, 739, 10.3390/en12040739

Marino, 2016, Building energy load forecasting using deep neural networks, IEEE Conf. of the Industrial Electronics Society, 7046

Mocanu, 2016, Deep learning for estimating building energy consumption, Sustainable Energy, Grids and Networks, 6, 91, 10.1016/j.segan.2016.02.005

Zhou, 2016, Learning deep features for discriminative localization, Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2921