A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings
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Marcel Antal | Tudor Cioara | Ionut Anghel | Ioan Salomie | Andreea Valeria Vesa | Claudia Pop | Nicoleta Ghitescu | I. Salomie | Marcel Antal | T. Cioara | I. Anghel | Claudia Pop | Nicoleta Ghitescu
[1] L. Mili,et al. Electric Load Forecasting Based on Statistical Robust Methods , 2011, IEEE Transactions on Power Systems.
[2] Muhd Zaimi Abd Majid,et al. A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .
[3] Federico Divina,et al. Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting , 2018 .
[4] Wan He,et al. Load Forecasting via Deep Neural Networks , 2017, ITQM.
[5] Jianhua Zhang,et al. A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids , 2014 .
[6] Lee-Ing Tong,et al. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..
[7] Wei-Chiang Hong,et al. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artific , 2011 .
[8] Chen Wang,et al. A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting , 2016 .
[9] Garrison W. Cottrell,et al. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.
[10] Hak-Keung Lam,et al. A novel genetic-algorithm-based neural network for short-term load forecasting , 2003, IEEE Trans. Ind. Electron..
[11] Lambros Ekonomou,et al. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models , 2008 .
[12] R.-H. Liang,et al. Fuzzy linear programming: an application to hydroelectric generation scheduling , 1994 .
[13] Mohammed E. El-Telbany,et al. Short-term forecasting of Jordanian electricity demand using particle swarm optimization , 2008 .
[14] S. Muto,et al. Regression based peak load forecasting using a transformation technique , 1994 .
[15] Asifullah Khan,et al. Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks , 2017 .
[16] M. El-Hawary,et al. Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation , 1993 .
[17] Yuan Zhang,et al. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.
[18] Shyh-Jier Huang,et al. Short-term load forecasting via ARMA model identification including non-Gaussian process considerations , 2003 .
[19] Chen Jie,et al. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization , 2018, Energy Conversion and Management.
[20] Saudi Arabia,et al. SHORT-TERM PEAK DEMAND FORECASTING IN FAST DEVELOPING UTILITY WITH INHERIT DYNAMIC LOAD CHARACTERISTICS , 1990 .
[21] Wil L. Kling,et al. Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach , 2015, ArXiv.
[22] Michael T. Manry,et al. Comparison of very short-term load forecasting techniques , 1996 .
[23] Ping-Feng Pai,et al. Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .
[24] Yitao Liu,et al. Deep learning based ensemble approach for probabilistic wind power forecasting , 2017 .
[25] I. J. Ramirez-Rosado,et al. Distribution planning of electric energy using fuzzy models , 1996 .
[26] 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.
[27] M. Crawford,et al. An Adaptive Nonlinear Predictor with Orthogonal Escalator Structure for Short-Term Load Forecasting , 1989, IEEE Power Engineering Review.
[28] Sheng Wanxing,et al. Short-term load forecasting using artificial immune network , 2002, Proceedings. International Conference on Power System Technology.
[29] Saifur Rahman,et al. Analysis and Evaluation of Five Short-Term Load Forecasting Techniques , 1989, IEEE Power Engineering Review.
[30] Jack Kelly,et al. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes , 2014, Scientific Data.
[31] Marimuthu Palaniswami,et al. Improving load forecasting based on deep learning and K-shape clustering , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[32] Nashat T. AL-Jallad,et al. Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model , 2018, Energies.
[33] Mauro Conti,et al. Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directionsand Research Directions , 2018 .
[34] Sen Guo,et al. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..
[35] Y.-Y. Hsu,et al. Fuzzy expert systems: an application to short-term load forecasting , 1992 .
[36] T. Hesterberg,et al. A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.
[37] Yueren Wang,et al. A novel ensemble learning approach to support building energy use prediction , 2018 .
[38] W. M. Grady,et al. Enhancement, implementation, and performance of an adaptive short-term load forecasting algorithm , 1991 .
[39] Irena Koprinska,et al. Combining pattern sequence similarity with neural networks for forecasting electricity demand time series , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[40] Lino Guzzella,et al. SHORT-TERM THERMAL AND ELECTRIC LOAD FORECASTING IN BUILDINGS , 2013 .
[41] Regina Lamedica,et al. A neural network based technique for short-term forecasting of anomalous load periods , 1996 .
[42] Haipeng Wang,et al. An Ensemble Model Based on Machine Learning Methods for Short-term Power Load Forecasting , 2018, IOP Conference Series: Earth and Environmental Science.