Combination of short-term load forecasting models based on a stacking ensemble approach
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Seungmin Rho | Eenjun Hwang | Jihoon Moon | Jehyeok Rew | Seung-Won Jung | Seungmin Rho | Eenjun Hwang | Jihoon Moon | Seung‐Won Jung | Jehyeok Rew
[1] Eenjun Hwang,et al. Recurrent inception convolution neural network for multi short-term load forecasting , 2019, Energy and Buildings.
[2] Yoshua Bengio,et al. Practical Recommendations for Gradient-Based Training of Deep Architectures , 2012, Neural Networks: Tricks of the Trade.
[3] Yi Song,et al. Smart building energy management based on network occupancy sensing , 2018 .
[4] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[5] Lino Guzzella,et al. SHORT-TERM THERMAL AND ELECTRIC LOAD FORECASTING IN BUILDINGS , 2013 .
[6] Seung-Il Moon,et al. Very short-term load forecasting based on a pattern ratio in an office building , 2016 .
[7] S. Karsoliya,et al. Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .
[8] Eenjun Hwang,et al. A short-term load forecasting scheme based on auto-encoder and random forest , 2018 .
[9] Federico Silvestro,et al. Electrical consumption forecasting in hospital facilities: An application case , 2015 .
[10] Eenjun Hwang,et al. 2-Stage Electric Load Forecasting Scheme for Day-Ahead CCHP Scheduling , 2019, 2019 IEEE 13th International Conference on Power Electronics and Drive Systems (PEDS).
[11] Eenjun Hwang,et al. Forecasting power consumption for higher educational institutions based on machine learning , 2018, The Journal of Supercomputing.
[12] Àngela Nebot,et al. Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques , 2015 .
[13] Siddharth Suryanarayanan,et al. An Artificial Neural Network in Short-Term Electrical Load Forecasting of a University Campus: A Case Study , 2013 .
[14] J. Mikulik. Energy Demand Patterns in an Office Building: A Case Study in Kraków (Southern Poland) , 2018, Sustainability.
[15] Frank Hutter,et al. Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves , 2015, IJCAI.
[16] Michael J. Pazzani,et al. A Principal Components Approach to Combining Regression Estimates , 1999, Machine Learning.
[17] Saifur Rahman,et al. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques , 2019, Applied Energy.
[18] Daniel L. Marino,et al. Building energy load forecasting using Deep Neural Networks , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.
[19] P. H. Nguyen,et al. Agent-based control for building energy management in the smart grid framework , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.
[20] Le Zhang,et al. Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).
[21] A. Ożadowicz,et al. A New Concept of Active Demand Side Management for Energy Efficient Prosumer Microgrids with Smart Building Technologies , 2017 .
[22] Tao Hong,et al. Probabilistic electric load forecasting: A tutorial review , 2016 .
[23] Jaime Lloret,et al. A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings , 2014, IEEE Communications Surveys & Tutorials.
[24] Olatunji Ruwase,et al. Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems , 2015, KDD.
[25] Erin LeDell,et al. Scalable Ensemble Learning and Computationally Efficient Variance Estimation , 2015 .
[26] Zafar A. Khan,et al. Load forecasting, dynamic pricing and DSM in smart grid: A review , 2016 .
[27] Khuram Pervez Amber,et al. Electricity consumption forecasting models for administration buildings of the UK higher education sector , 2015 .
[28] Milos Manic,et al. Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings , 2016, IEEE Industrial Electronics Magazine.
[29] S. Chatterjee,et al. Regression Analysis by Example , 1979 .
[30] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[31] Eenjun Hwang,et al. A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics , 2018, 2018 IEEE International Conference on Big Data and Smart Computing (BigComp).
[32] Nashat T. AL-Jallad,et al. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model , 2018, Energies.
[33] A. Ferrer,et al. PCA model building with missing data: New proposals and a comparative study , 2015 .
[34] Eenjun Hwang,et al. Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron , 2018, Energies.
[35] Jose I. Bilbao,et al. A review and analysis of regression and machine learning models on commercial building electricity load forecasting , 2017 .
[36] Gaurav Pandey,et al. A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics , 2013, 2013 IEEE 13th International Conference on Data Mining.
[37] Kevin M. Smith,et al. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy , 2014 .
[38] Yacine Rezgui,et al. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .
[39] Fu Xiao,et al. A short-term building cooling load prediction method using deep learning algorithms , 2017 .
[40] George Edwards,et al. A Review of Deep Learning Methods Applied on Load Forecasting , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).
[41] Miriam A. M. Capretz,et al. Energy Forecasting for Event Venues: Big Data and Prediction Accuracy , 2016 .
[42] Yuan Zhang,et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.
[43] Ran Li,et al. Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN , 2018, IEEE Transactions on Smart Grid.
[44] Lachlan L. H. Andrew,et al. Short-term residential load forecasting: Impact of calendar effects and forecast granularity , 2017 .
[45] Heaton T. Jeff,et al. Introduction to Neural Networks with Java , 2005 .
[46] Michael J. Crawley,et al. The R book , 2022 .
[47] Leandro dos Santos Coelho,et al. Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting , 2019, Eng. Appl. Artif. Intell..
[48] Ashok K. Bhateja,et al. ANN Based Distinguishing Attack on RC4 Stream Cipher , 2012, BIC-TA.
[49] Guohai Liu,et al. Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis , 2015 .
[50] Yibo Chen,et al. Short-term prediction of electric demand in building sector via hybrid support vector regression , 2017 .
[51] Hongseok Kim,et al. Deep Neural Network Based Demand Side Short Term Load Forecasting , 2016 .
[52] Ping Wang,et al. Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose , 2018, PloS one.
[53] Federico Divina,et al. Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting , 2018 .