Year  Title  Author 
2017

Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes 
E Azarkhish, D Rossi, I Loi, L Benini

2017

A Heterogeneous MultiCore SystemonChip for Energy Efficient Brain Inspired Computing 
A Pullini, F Conti, D Rossi, I Loi, M Gautschi, L Benini

2017

A Model of EnergyAwareness Predictor to Improve the Energy Efficiency 
S Kim, YI Yoon

2017

Mining Big Building Operational Data for Building Cooling Load Prediction and Energy Efficiency Improvement 
F Xiao, S Wang, C Fan

2017

Deep Learning Based Intelligent Basketball Arena with Energy Image 
W Liu, J Liu, X Gu, K Liu, X Dai, H Ma

2017

Shortterm Wind Energy Prediction Algorithm Based on SAGADBNs 
W Fei, WU Zhong

2017

An Empirical Study on Energy Disaggregation via Deep Learning 
W He, Y Chai

2017

Energy efficient HVAC control for an IPSenabled large space in commercial buildings through dynamic spatial occupancy distribution 
W Wang, J Chen, G Huang, Y Lu

2017

CaloGAN: Simulating 3D High Energy Particle Showers in MultiLayer Electromagnetic Calorimeters with Generative Adversarial Networks 
M Paganini, L de Oliveira, B Nachman

2017

Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE 
Q Liu, D Wang

2017

Monthly energy consumption forecast: A deep learning approach 
RF Berriel, AT Lopes, A Rodrigues, FM Varejão

2017

MultiObjective Optimization of Hybrid Renewable Energy System with Load Forecasting 
M Ming, R Wang, Y Zha, T Zhang

2016

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity 
T Mesnard, W Gerstner, J Brea

2016

Unsupervised energy prediction in a Smart Grid context using reinforcement crossbuilding transfer learning 
E Mocanu, PH Nguyen, WL Kling, M Gibescu

2016

Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs 
D Li, X Chen, M Becchi, Z Zong

2016

Addressing the main challenges of energy security in the twentyfirst century–Contributions of the conferences on Sustainable Development of Energy, Water and … 
N Markovska, N Duić, BV Mathiesen, Z Guzović

2016

Unsupervised energy prediction under smart grid context using reinforcement cross buildings transfer learning 
E Mocanu, PH Nguyen, WL Kling, M Gibescu

2016

Building energy modeling (BEM) using clustering algorithms and semisupervised machine learning approaches 
H Naganathan, WO Chong, X Chen

2016

Deep Learning to estimate building energy demands in the smart grid context 
E Mocanu, PH Nguyen, M Gibescu, W Kling

2016

Generative Adversarial Networks as Variational Training of Energy Based Models 
S Zhai, Y Cheng, R Feris, Z Zhang

2016

Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques 
T Sogabe, H Ichikawa, K Sakamoto, K Yamaguchi

2016

Effects of Security and Privacy Concerns on using of Cloud Services in Energy Industry, an Oil and Gas Company: A Case Study 
A Poorebrahimi, F SoleimaniRoozbahani

2016

Bioinspired system architecture for energy efficient, BIGDATA computing with application to wide area motion imagery 
AG Andreou, T Figliolia, K Sanni, TS Murray, G Tognetti

2016

Deep Wavelet Scattering for Quantum Energy Regression 
M Hirn

2016

Memory system optimizations for energy and bandwidth efficient data movement 
M Nazm Bojnordi

2016

PCANet: An energy perspective 
J Wu, S Qiu, Y Kong, L Jiang, L Senhadji, H Shu

2016

Deep learning for estimating building energy consumption 
E Mocanu, P Nguyen, M Gibescu, WL Kling

2016

Big IoT data mining for realtime energy disaggregation in buildings 
DC Mocanu, E Mocanu, PH Nguyen, M Gibescu

2016

Smart Tiles—an Energy Infrastructure for the Solar System 
H Bloom

2016

DeLight: Adding Energy Dimension To Deep Neural Networks 
BD Rouhani, A Mirhoseini, F Koushanfar

2016

Enhancing Energy Minimization Framework for Scene Text Recognition with TopDown Cues 
A Mishra, K Alahari, CV Jawahar

2016

Saving Energy in QoS Networked Data Centers 
M Shojafar

2016

Improving energy efficiency and classification accuracy of neuromorphic chips by learning binary synaptic crossbars 
AJ Yepes, J Tang

2016

Energy Internet: The business perspective 
K Zhou, S Yang, Z Shao

2015

Generalized approach to energy distribution of spin system 
B Kryzhanovsky, L Litinskii

2015

Quantum Energy Regression using Scattering Transforms 
M Hirn, N Poilvert, S Mallat

2015

Deadlineaware task scheduling for solarpowered nonvolatile sensor nodes with global energy migration 
D Zhang, Y Liu, X Sheng, J Li, T Wu, CJ Xue, H Yang

2015

Training Restricted Boltzmann Machines via the ThoulessAndersonPalmer Free Energy 
M Gabrié, EW Tramel, F Krzakala

2015

Deep Neural Networks for Wind Energy Prediction 
D Díaz, A Torres, JR Dorronsoro

2015

The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks 
K Yao, J Parkhill

2015

Data Center Energy Consumption Modeling: A Survey 
M Dayarathna, Y Wen, R Fan

2015

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation 
J Kelly, W Knottenbelt

2015

Generalized approach to description of energy distribution of spin system 
B Kryzhanovsky, L Litinskii

2015

Semisupervised Energy Modeling (SSEM) for Building Clusters Using Machine Learning Techniques 
H Naganathan, WK Chong, X Chen

2015

Neural Conditional Energy Models for Multilabel Classification 
H Jing, SD Lin

2015

Using energy profiles to identify university energy reduction opportunities 
N Maistry, H Annegarn

2015

Efficient Generation of Energy and Performance Pareto Front for FPGA Designs 
SR Kuppannagari, VK Prasanna

2015

Structured Prediction Energy Networks 
D Belanger, A McCallum

2015

Big Data Analytic: Cases for Communications Systems Modeling and Renewable Energy Forecast 
YS Manjili, M Niknamfar

2015

A common approach to intelligent energy and mobility services in a smart city environment 
M Lützenberger, N Masuch, T Küster, D Freund, M Voß

2014

Deep Belief Network Training Improvement Using Elite Samples Minimizing Free Energy 
MA Keyvanrad, MM Homayounpour

2014

Atomic Energy Models For Machine Learning: Atomic Restricted Boltzmann Machines 
H Tosun

2014

The Potential Energy of an Autoencoder 
H Kamyshanska, R Memisevic

2014

Energy Based Models and Boltzmann Machines (Cont.) 
W Adams, K Plis
