Deep and Efficient Impact Models for Edge Characterization and Control of Energy Events
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[1] Francesco Borrelli,et al. Model Predictive Control of thermal energy storage in building cooling systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.
[2] Jui-Sheng Chou,et al. Real-time detection of anomalous power consumption , 2014 .
[3] Phuong H. Nguyen,et al. Collaborative learning for classification and prediction of building energy flexibility , 2019, 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe).
[4] Clayton Miller,et al. What's in the box?! Towards explainable machine learning applied to non-residential building smart meter classification , 2019, Energy and Buildings.
[5] Sanjeevikumar Padmanaban,et al. Study and Analysis of an Intelligent Microgrid Energy Management Solution with Distributed Energy Sources , 2017 .
[6] Albert Y. Zomaya,et al. From Insight to Impact: Building a Sustainable Edge Computing Platform for Smart Homes , 2018, 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS).
[7] Jui-Sheng Chou,et al. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders , 2018, Energy.
[8] Jian LUO,et al. Real-time anomaly detection for very short-term load forecasting , 2018 .
[9] Margarida Silveira,et al. Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).
[10] Alex Sherstinsky,et al. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network , 2018, Physica D: Nonlinear Phenomena.
[11] Yanghui Wu,et al. A XGBoost Model with Weather Similarity Analysis and Feature Engineering for Short-Term Wind Power Forecasting , 2019, Applied Sciences.
[12] Xiandong Xu,et al. Hierarchical microgrid energy management in an office building , 2017 .
[13] A. Stephen McGough,et al. Segmenting Residential Smart Meter Data for Short-Term Load Forecasting , 2018, e-Energy.
[14] Pushpendra Singh,et al. Rimor: towards identifying anomalous appliances in buildings , 2018, BuildSys@SenSys.
[15] S. Beck,et al. Using regression analysis to predict the future energy consumption of a supermarket in the UK , 2014 .
[16] Florian Ziel,et al. Forecasting Electricity Spot Prices Using Lasso: On Capturing the Autoregressive Intraday Structure , 2015, IEEE Transactions on Power Systems.
[17] Antonio F. Gómez-Skarmeta,et al. A methodology for energy multivariate time series forecasting in smart buildings based on feature selection , 2019, Energy and Buildings.
[18] Hao Wang,et al. A New Anomaly Detection System for School Electricity Consumption Data , 2017, Inf..
[19] Yang Zhao,et al. Deep learning-based feature engineering methods for improved building energy prediction , 2019, Applied Energy.
[20] Andreas W. Kempa-Liehr,et al. Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh - A Python package) , 2018, Neurocomputing.
[21] Nadeem Javaid,et al. Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids , 2019, Electronics.
[22] Iulia Stamatescu,et al. Decision Support System for a Low Voltage Renewable Energy System , 2017 .
[23] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[24] Grigore Stamatescu,et al. Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting , 2019, Inf..
[25] 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 .
[26] Aven Satre-Meloy,et al. Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models , 2019, Energy.
[27] Clayton Miller,et al. The Building Data Genome Project: An open, public data set from non-residential building electrical meters , 2017 .