MITP-Net: A Deep-Learning Framework for Short-Term Indoor Temperature Predictions in Multi-Zone Buildings

[1]  Qianchuan Zhao,et al.  Honeycomb: An open-source distributed system for smart buildings , 2022, Patterns.

[2]  H. Pandžić,et al.  Learning indoor temperature predictions for optimal load ensemble control , 2022, Electric Power Systems Research.

[3]  Hongwei Gong,et al.  Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction , 2022, Building and Environment.

[4]  Junping Liang,et al.  Improving indoor air flow and temperature prediction with local measurements based on CFD-EnKF data assimilation , 2022, Building and Environment.

[5]  Peng Wang,et al.  A strategy of improving indoor air temperature prediction in HVAC system based on multivariate transfer entropy , 2022, Building and Environment.

[6]  A. Ardiansyah,et al.  A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting , 2022, Energies.

[7]  Xiaoping Zhou,et al.  Real-time prediction of indoor humidity with limited sensors using cross-sample learning , 2022, Building and Environment.

[8]  Urbain Nzotcha,et al.  Explaining household electricity consumption using quantile regression, decision tree and artificial neural network , 2022, Energy.

[9]  R. Arcucci,et al.  CntrlDA: A building energy management control system with real-time adjustments. Application to indoor temperature , 2022, Building and Environment.

[10]  B. Lin,et al.  Thermal preference prediction based on occupants’ adaptive behavior in indoor environments- A study of an air-conditioned multi-occupancy office in China , 2021, Building and Environment.

[11]  P. Hellinckx,et al.  CNN-LSTM architecture for predictive indoor temperature modeling , 2021, Building and Environment.

[12]  Reinhard Radermacher,et al.  Comparing deep learning models for multi energy vectors prediction on multiple types of building , 2021 .

[13]  Seunghoon Jung,et al.  Occupant-centered real-time control of indoor temperature using deep learning algorithms , 2021, Building and Environment.

[14]  Ali Razban,et al.  Learning-based CO2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation , 2021 .

[15]  Zhang Lin,et al.  Modelling indoor environment indicators using artificial neural network in the stratified environments , 2021, Building and Environment.

[16]  Xiaoteng Ma,et al.  Mpsn: Motion-Aware Pseudo Siamese Network for Indoor Video Head Detection , 2021, SSRN Electronic Journal.

[17]  Yingjun Ruan,et al.  Interpretable deep learning model for building energy consumption prediction based on attention mechanism , 2021 .

[18]  L. Idahosa,et al.  A social constructionist approach to managing HVAC energy consumption using social norms – A randomised field experiment , 2021, Energy Policy.

[19]  Chengying Qi,et al.  A Dynamic Control Strategy of District Heating Substations Based on Online Prediction and Indoor Temperature Feedback , 2021 .

[20]  Benoit Delinchant,et al.  Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model , 2021 .

[21]  Jianlong Xu,et al.  FM-GRU: A Time Series Prediction Method for Water Quality Based on seq2seq Framework , 2021, Water.

[22]  Amjad J. Humaidi,et al.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions , 2021, Journal of Big Data.

[23]  Claudio Del Pero,et al.  Smart buildings features and key performance indicators: A review , 2020 .

[24]  Jianhong Zou,et al.  A review of building occupancy measurement systems , 2020 .

[25]  Mary Ann Piette,et al.  Building thermal load prediction through shallow machine learning and deep learning , 2020, Applied Energy.

[26]  M. Santamouris,et al.  Occupancy-based zone-level VAV system control implications on thermal comfort, ventilation, indoor air quality and building energy efficiency , 2019 .

[27]  Zhiyuan Zhang,et al.  Understanding and Improving Layer Normalization , 2019, NeurIPS.

[28]  Man Pun Wan,et al.  Experimental study of a model predictive control system for active chilled beam (ACB) air-conditioning system , 2019, Energy and Buildings.

[29]  Chen Ren,et al.  Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control , 2019, Sustainable Cities and Society.

[30]  Anuj Kumar,et al.  Sensing, Controlling, and IoT Infrastructure in Smart Building: A Review , 2019, IEEE Sensors Journal.

[31]  Alexis K.H. Lau,et al.  Energy consumption, indoor thermal comfort and air quality in a commercial office with retrofitted heat, ventilation and air conditioning (HVAC) system , 2019, Energy and Buildings.

[32]  Lihua Xie,et al.  A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings , 2019, Applied Energy.

[33]  Henry Leung,et al.  A Review of Deep Learning Models for Time Series Prediction , 2019, IEEE Sensors Journal.

[34]  Yu Pan,et al.  District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model , 2019, Energies.

[35]  Constantine E. Kontokosta,et al.  Rethinking HVAC temperature setpoints in commercial buildings: The potential for zero-cost energy savings and comfort improvement in different climates , 2019, Building and Environment.

[36]  Yilun Du,et al.  Task-Agnostic Dynamics Priors for Deep Reinforcement Learning , 2019, ICML.

[37]  Lei Xu,et al.  Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load , 2019, Applied Energy.

[38]  Yue Yuan,et al.  Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method , 2019, Building and Environment.

[39]  Gary Higgins,et al.  Real-time prediction model for indoor temperature in a commercial building , 2018, Applied Energy.

[40]  Ernestina Menasalvas Ruiz,et al.  Indoor Temperature Prediction in an IoT Scenario , 2018, Sensors.

[41]  Jili Zhang,et al.  An identification method for room temperature dynamic model based on analytical solution in VAV system , 2018, Energy and Buildings.

[42]  Zhe Zhang,et al.  A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings , 2018, Energy and Buildings.

[43]  Jun Wu,et al.  Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system , 2018, Appl. Soft Comput..

[44]  Chung Choo Chung,et al.  Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[45]  Haoxiang Wang,et al.  Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings , 2017, Future Gener. Comput. Syst..

[46]  Vítor Leal,et al.  Modelling the relationship between heating energy use and indoor temperatures in residential buildings through Artificial Neural Networks considering occupant behavior , 2017 .

[47]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[48]  Fathi M. Salem,et al.  Gate-variants of Gated Recurrent Unit (GRU) neural networks , 2017, 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

[49]  Kazem Sohraby,et al.  IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems , 2017, IEEE Internet of Things Journal.

[50]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[51]  Aaron C. Courville,et al.  Professor Forcing: A New Algorithm for Training Recurrent Networks , 2016, NIPS.

[52]  Leopold,et al.  Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region , 2016 .

[53]  Nuno M. Mateus,et al.  Validation of a lumped RC model for thermal simulation of a double skin natural and mechanical ventilated test cell , 2016 .

[54]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[55]  Bing Dong,et al.  A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting , 2013, Building Simulation.

[56]  Victor M. Zavala,et al.  Gaussian process modeling for measurement and verification of building energy savings , 2012 .

[57]  José Manuel Benítez,et al.  On the use of cross-validation for time series predictor evaluation , 2012, Inf. Sci..

[58]  Mohan S. Kankanhalli,et al.  Multimodal fusion for multimedia analysis: a survey , 2010, Multimedia Systems.

[59]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[60]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[61]  Bertil Thomas,et al.  Artificial neural network models for indoor temperature prediction: investigations in two buildings , 2006, Neural Computing and Applications.

[62]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[63]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[64]  Helia Zandi,et al.  Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning , 2021 .

[65]  Jiankang Ren,et al.  Experimental study of an indoor temperature fuzzy control method for thermal comfort and energy saving using wristband device , 2021 .