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Next-generation energy systems for sustainable smart cities: Roles of transfer learning

SUSTAINABLE CITIES AND SOCIETY (2022)

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Article Construction & Building Technology

Transfer learning based methodology for migration and application of fault detection and diagnosis between building chillers for improving energy efficiency

Xu Zhu et al.

Summary: This paper proposes a generic framework for transferring prior knowledge from a source chiller to build a diagnostic model for a new target chiller. The method involves standardizing heterogeneous data, applying domain adaption transfer learning to overcome domain shift, and utilizing domain adversarial neural network to generate the diagnostic model for the target chiller. Results show that the transferred diagnostic model yields decent performance, outperforming conventional machine learning models.

BUILDING AND ENVIRONMENT (2021)

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Article Automation & Control Systems

Data-driven fault diagnosis for heterogeneous chillers using domain adaptation techniques

Ron van de Sand et al.

Summary: This paper introduces a data-driven fault diagnosis model using domain adaptation techniques to transfer knowledge without labeled data, improving accuracy and applicability in diagnosing chiller faults.

CONTROL ENGINEERING PRACTICE (2021)

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Article Energy & Fuels

Edge HVAC Analytics

Ioan Petri et al.

Summary: Integrating data analytics, optimisation and dynamic control to support energy services has attracted significant interest in recent years. The combination of IoT and edge computing allows connectivity between energy systems and infrastructure, enabling the implementation of energy efficiency/optimisation and cost reduction strategies.

ENERGIES (2021)

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Article Energy & Fuels

A Procedure for Automating Energy Analyses in the BIM Context Exploiting Artificial Neural Networks and Transfer Learning Technique

Mikhail Demianenko et al.

Summary: Building Information Modelling benefits decision-making by testing digital twins for energy consumption and sustainability. Challenges include considering numerous elements and parameters for energy balance adjustment.

ENERGIES (2021)

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Article Construction & Building Technology

Development of a back-propagation neural network and adaptive grey wolf optimizer algorithm for thermal comfort and energy consumption prediction and optimization

Lu Li et al.

Summary: This study aims to propose a rapid prediction and optimization algorithm for indoor thermal comfort levels, while minimizing energy consumption to achieve a balance between thermal comfort and energy savings.

ENERGY AND BUILDINGS (2021)

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Article Engineering, Electrical & Electronic

A novel adversarial transfer learning in deep convolutional neural network for intelligent diagnosis of gas-insulated switchgear insulation defect A DATCNN for GIS insulation defect diagnosis

Yanxin Wang et al.

Summary: The proposed method, DATCNN, is a novel domain adversarial transfer convolutional neural network that can diagnose GIS insulation defects using small samples. By introducing a domain adversarial training strategy, it achieves simultaneous adaptation of features and labels, leading to very high diagnosis accuracy.

IET GENERATION TRANSMISSION & DISTRIBUTION (2021)

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Article Computer Science, Artificial Intelligence

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Yassine Himeur et al.

Summary: In recent years, recommender systems have developed significantly alongside the advancements in IoT and AI technologies. In the building sector, energy efficiency has become a hot research topic where recommender systems play a major role in promoting energy saving behavior. However, further investigations and solutions are needed to address challenges and enable the widespread adoption of this technology.

INFORMATION FUSION (2021)

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Article Green & Sustainable Science & Technology

A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids

Sheraz Aslam et al.

Summary: Microgrids combining renewable energy sources, energy storage devices, and load management methods face challenges due to the intermittent nature of renewables. Forecasting power generation from renewables is crucial for efficient grid operations and optimal resource utilization. Machine learning and deep learning models show promise in predicting energy demand and generation, with the efficiency of forecasting methods depending on historical data availability.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

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Article Green & Sustainable Science & Technology

Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data

Yanting Li et al.

Summary: This paper proposes a fault diagnosis method for wind turbines based on parameter-based transfer learning and convolutional autoencoder, suitable for small-scale data. The method can transfer knowledge from similar wind turbines and shows advantages in fault diagnosis.

RENEWABLE ENERGY (2021)

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Article Chemistry, Analytical

An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks

Huanyue Liao et al.

Summary: An online data-driven diagnosis method combining rule-based method and CNNs was proposed for fault diagnosis of AHU in HVAC systems, achieving an accurate identification of fault types with a 99.15% accuracy in offline testing and fast online detection. Experimental results validate the performance improvement of the proposed RACNN method.

SENSORS (2021)

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Article Construction & Building Technology

Parameter estimation of unknown properties using transfer learning from virtual to existing buildings

Yun-Dam Ko et al.

Summary: This study successfully applies transfer learning to identify unknown building properties, achieving significant improvements in identifying wall U-value, HVAC efficiency, and lighting power density. The use of transfer learning enables the developed model to be reusable for another group of buildings, improving performance and reducing training time.

JOURNAL OF BUILDING PERFORMANCE SIMULATION (2021)

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Article Automation & Control Systems

Deep-Learning-Based Probabilistic Forecasting of Electric Vehicle Charging Load With a Novel Queuing Model

Xian Zhang et al.

Summary: This article predicts traffic flow using deep learning and proposes a new probabilistic queuing model for charging load forecasting. Experimental results show that this method can comprehensively learn the uncertainties of electric vehicle charging load, indicating significant potential for practical applications.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

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Article Green & Sustainable Science & Technology

Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

Mingzhi Yang et al.

Summary: This paper introduces a deep neural network model based on an attention mechanism for nonintrusive load monitoring, which significantly improves the performance of traditional models, and transfer learning can effectively enhance the prediction ability.

SUSTAINABILITY (2021)

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Article Chemistry, Multidisciplinary

Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning

Danpeng Cheng et al.

Summary: This study demonstrates the potential of data-driven machine learning algorithms in predicting solid-state battery lifetimes, showing successful predictions with symbolic regression and providing a new approach for battery classification, utilization, and recycling.

APPLIED SCIENCES-BASEL (2021)

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Article Computer Science, Information Systems

Multipath TCP Meets Transfer Learning: A Novel Edge-Based Learning for Industrial IoT

Shiva Raj Pokhrel et al.

Summary: This study focuses on a novel distributed transfer learning framework for Industry 4.0, aiming to accelerate learning efficiency and enhance performance by transferring knowledge from expert machines to newly deployed ones. The approach is validated through numerical and emulated NS-3 experiments, showing promising results compared to state-of-the-art schemes.

IEEE INTERNET OF THINGS JOURNAL (2021)

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Article Construction & Building Technology

Stochastic energy management and scheduling of microgrids in correlated environment: A deep learning-oriented approach

Tan Cheng et al.

Summary: This paper proposes a secured stochastic energy management scheme for hybrid AC-DC microgrids considering renewable energy sources, plug-in hybrid electric vehicles, and energy storage devices. The operation of microgrids is formulated as a single objective optimization problem solved using an interior search optimization algorithm. Additionally, a deep learning-based intrusion detection system is proposed to enhance data security in microgrids, with the performance of the scheme evaluated using the IEEE 33 bus test system.

SUSTAINABLE CITIES AND SOCIETY (2021)

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Article Construction & Building Technology

Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies

Imran Ahmed et al.

Summary: This paper introduces a human detection system based on deep neural networks using an overhead perspective for intelligent surveillance in smart cities and societies. By utilizing the Gaussian YOLOv3 algorithm to improve the human detection system, the experimental results show an overall detection accuracy of 94%.

SUSTAINABLE CITIES AND SOCIETY (2021)

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Article Construction & Building Technology

Automated rail surface crack analytics using deep data-driven models and transfer learning

Zhong Zheng et al.

Summary: A deep transfer learning framework for rail surface crack detection is developed in this study, utilizing pre-trained deep learning models based on YOLOv3 and RetinaNet for improved performance. The DTL model outperforms benchmarking models in recall and average precision, while traditional methods like VDS and GEA show poor performance in crack detection tasks. Additionally, YOLOv3 is better at detecting small cracks, while RetinaNet performs better on larger cracks.

SUSTAINABLE CITIES AND SOCIETY (2021)

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Article Energy & Fuels

Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning

Seongyoon Kim et al.

Summary: We propose a deep-learning based method for accurately predicting state-of-health and remaining useful life of Lithium-ion batteries, utilizing transfer learning to predict different battery types' states. The proposed method also estimates predictive uncertainty and degradation patterns, demonstrating reliability and accuracy in forecasting even for used batteries. Simulation results showcase the effectiveness of the model in reducing data collection efforts for new battery types.

JOURNAL OF ENERGY STORAGE (2021)

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Article Computer Science, Information Systems

Deep Transfer Learning-Based Feature Extraction: An Approach to Improve Nonintrusive Load Monitoring

Diego L. Cavalca et al.

Summary: This study proposed a nonintrusive load monitoring method based on convolutional neural networks, which successfully labeled and classified loads through three stages of processing, demonstrating a high level of accuracy in experiments.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic

Calendar Ageing Model for Li-Ion Batteries Using Transfer Learning Methods

Markel Azkue et al.

Summary: This paper proposes a transfer learning (TL) method using Neural Networks models to develop LIB ageing models, successfully leveraging experimental laboratory testing data previously obtained for a different cell technology and achieving a low overall error.

WORLD ELECTRIC VEHICLE JOURNAL (2021)

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Article Energy & Fuels

Transfer learning applied to DRL-Based heat pump control to leverage microgrid energy efficiency

Paulo Lissa et al.

Summary: This study investigates the application of transfer learning to deep reinforcement learning-based heat pump control in order to improve energy efficiency in a microgrid. The experiments show that the proposed algorithm achieved up to 10% savings after transfer learning was applied, contributing to load-shifting and reducing the learning time by more than a factor of 5.

SMART ENERGY (2021)

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Article Computer Science, Artificial Intelligence

The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency

Christos Sardianos et al.

Summary: Recent advances in artificial intelligence, especially in machine learning and deep learning, have improved the performance of intelligent systems and increased the demand for understanding system reasoning. Explainability in recommendation systems is essential to enhance user trust and acceptance.

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning

Qiming Fu et al.

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE (2020)

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Article Computer Science, Artificial Intelligence

Online transfer learning with multiple source domains for multi-class classification

Zhongfeng Kang et al.

KNOWLEDGE-BASED SYSTEMS (2020)

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Article Engineering, Multidisciplinary

Online detection for bearing incipient fault based on deep transfer learning

Wentao Mao et al.

MEASUREMENT (2020)

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Article Energy & Fuels

NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid

Laya Das et al.

APPLIED ENERGY (2020)

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Article Energy & Fuels

Statistical investigations of transfer learning-based methodology for short-term building energy predictions

Cheng Fan et al.

APPLIED ENERGY (2020)

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Article Engineering, Electrical & Electronic

A hybrid transfer learning model for short-term electric load forecasting

Xianze Xu et al.

ELECTRICAL ENGINEERING (2020)

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Article Energy & Fuels

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

Jihoon Moon et al.

ENERGIES (2020)

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Article Construction & Building Technology

The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls

Jack Ngarambe et al.

ENERGY AND BUILDINGS (2020)

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Article Engineering, Multidisciplinary

An adaptive deep transfer learning method for bearing fault diagnosis

Zhenghong Wu et al.

MEASUREMENT (2020)

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Article Engineering, Electrical & Electronic

Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting

Di Wu et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

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Article Engineering, Electrical & Electronic

Transfer Learning for Non-Intrusive Load Monitoring

Michele D'Incecco et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

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Article Green & Sustainable Science & Technology

Transfer learning with deep neural networks for model predictive control of HVAC and natural ventilation in smart buildings

Yujiao Chen et al.

JOURNAL OF CLEANER PRODUCTION (2020)

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Article Chemistry, Analytical

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Tuong Le et al.

SENSORS (2020)

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Article Energy & Fuels

Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

Yassine Himeur et al.

APPLIED ENERGY (2020)

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Article Computer Science, Interdisciplinary Applications

Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes

Weijun Li et al.

COMPUTERS & CHEMICAL ENGINEERING (2020)

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Article Energy & Fuels

Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks

Giorgio Guariso et al.

ENERGIES (2020)

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Article Construction & Building Technology

A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data

Jun Ma et al.

ENERGY AND BUILDINGS (2020)

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Article Green & Sustainable Science & Technology

Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

Seung-Min Jung et al.

SUSTAINABILITY (2020)

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Article Construction & Building Technology

Blockchain and federated learning-based distributed computing defence framework for sustainable society

Pradip Kumar Sharma et al.

SUSTAINABLE CITIES AND SOCIETY (2020)

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Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

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Article Construction & Building Technology

Deep learning and transfer learning models of energy consumption forecasting for a building with poor information data

Yuan Gao et al.

ENERGY AND BUILDINGS (2020)

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Article Computer Science, Hardware & Architecture

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Abu Sufian et al.

JOURNAL OF SYSTEMS ARCHITECTURE (2020)

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Article Computer Science, Artificial Intelligence

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Yassine Himeur et al.

COGNITIVE COMPUTATION (2020)

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Article Computer Science, Information Systems

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

Guanyu Gao et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

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Article Computer Science, Artificial Intelligence

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Yassine Himeur et al.

INFORMATION FUSION (2020)

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Article Construction & Building Technology

Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city

Saurabh Singh et al.

SUSTAINABLE CITIES AND SOCIETY (2020)

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Article Environmental Studies

Beyond energy savings: The necessity of optimising smart electricity systems with resource efficiency and coherent waste policy in Europe

Heyd F. Mas et al.

ENERGY RESEARCH & SOCIAL SCIENCE (2020)

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Article Computer Science, Information Systems

Cooperating Edge Cloud-Based Hybrid Online Learning for Accelerated Energy Data Stream Processing in Load Forecasting

Changha Lee et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Intelligent Cooperative Edge Computing in Internet of Things

Chao Gong et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

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Review Construction & Building Technology

Building power consumption datasets: Survey, taxonomy and future directions

Yassine Himeur et al.

ENERGY AND BUILDINGS (2020)

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Article Construction & Building Technology

Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid

Muhammad Babar et al.

SUSTAINABLE CITIES AND SOCIETY (2020)

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Article Computer Science, Information Systems

Achieving Domestic Energy Efficiency Using Micro-Moments and Intelligent Recommendations

Abdullah Alsalemi et al.

IEEE ACCESS (2020)

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Article Energy & Fuels

Deep learning-based feature engineering methods for improved building energy prediction

Cheng Fan et al.

APPLIED ENERGY (2019)

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Article Construction & Building Technology

Tuning machine learning models for prediction of building energy loads

Saleh Seyedzadeh et al.

SUSTAINABLE CITIES AND SOCIETY (2019)

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Article Thermodynamics

Transfer-learning based gas path analysis method for gas turbines

Shanxuan Tang et al.

APPLIED THERMAL ENGINEERING (2019)

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Review Engineering, Electrical & Electronic

Deep Learning With Edge Computing: A Review

Jiasi Chen et al.

PROCEEDINGS OF THE IEEE (2019)

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Article Engineering, Electrical & Electronic

Non-Intrusive Load Monitoring by Voltage-Current Trajectory Enabled Transfer Learning

Yanchi Liu et al.

IEEE TRANSACTIONS ON SMART GRID (2019)

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Review Thermodynamics

A review of deep learning for renewable energy forecasting

Huaizhi Wang et al.

ENERGY CONVERSION AND MANAGEMENT (2019)

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Article Multidisciplinary Sciences

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Dipendra Jha et al.

NATURE COMMUNICATIONS (2019)

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Article Construction & Building Technology

Analysis of thermal energy saving potentials through adjusting user behavior in hotel buildings of the Yangtze River region

Yichao Wang et al.

SUSTAINABLE CITIES AND SOCIETY (2019)

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Article Energy & Fuels

A deep learning method for online capacity estimation of lithium-ion batteries

Sheng Shen et al.

JOURNAL OF ENERGY STORAGE (2019)

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Article Energy & Fuels

Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees

Long Cai et al.

ENERGIES (2019)

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Article Engineering, Multidisciplinary

A new wind turbine health condition monitoring method based on VMD-MPE and feature-based transfer learning

He Ren et al.

MEASUREMENT (2019)

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Proceedings Paper Computer Science, Theory & Methods

Heterogeneous Transfer Learning for Thermal Comfort Modeling

Weizheng Hu et al.

BUILDSYS'19: PROCEEDINGS OF THE 6TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION (2019)

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Proceedings Paper Computer Science, Theory & Methods

Towards reproducible state-of-the-art energy disaggregation

Nipun Batra et al.

BUILDSYS'19: PROCEEDINGS OF THE 6TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION (2019)

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Proceedings Paper Computer Science, Theory & Methods

Demo Abstract: A demonstration of reproducible state-of-the-art energy disaggregation using NILMTK

Nipun Batra et al.

BUILDSYS'19: PROCEEDINGS OF THE 6TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION (2019)

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Proceedings Paper Computer Science, Artificial Intelligence

Learning Across Tasks and Domains

Pierluigi Zama Ramirez et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

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Proceedings Paper Computer Science, Theory & Methods

Energy Predictive Models with Limited Data using Transfer Learning

Ali Hooshmand et al.

E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS (2019)

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Article Computer Science, Theory & Methods

Using transfer learning for smart building management system

Bens Pardamean et al.

JOURNAL OF BIG DATA (2019)

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Article Computer Science, Information Systems

Similarity-Based Chained Transfer Learning for Energy Forecasting With Big Data

Yifang Tian et al.

IEEE ACCESS (2019)

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Article Computer Science, Interdisciplinary Applications

Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks

Peng Liang et al.

INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2018)

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Article Computer Science, Artificial Intelligence

Negative transfer detection in transductive transfer learning

Lin Gui et al.

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2018)

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Article Construction & Building Technology

Transfer learning with seasonal and trend adjustment for cross-building energy forecasting

Mauro Ribeiro et al.

ENERGY AND BUILDINGS (2018)

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Review Engineering, Electrical & Electronic

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Van Nhan Nguyen et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2018)

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Article Computer Science, Artificial Intelligence

A novel transfer learning framework for time series forecasting

Rui Ye et al.

KNOWLEDGE-BASED SYSTEMS (2018)

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Article Computer Science, Information Systems

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Mehdi Mohammadi et al.

IEEE INTERNET OF THINGS JOURNAL (2018)

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Article Biophysics

Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India

Bappa Das et al.

INTERNATIONAL JOURNAL OF BIOMETEOROLOGY (2018)

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Article Computer Science, Information Systems

SSDS: A Smart Software-Defined Security Mechanism for Vehicle-to-Grid Using Transfer Learning

Shen Wang et al.

IEEE ACCESS (2018)

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Proceedings Paper Energy & Fuels

UniversalNILM: A Semi-supervised Energy Disaggregation Framework using General Appliance Models

Bontor Humala et al.

E-ENERGY'18: PROCEEDINGS OF THE 9TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS (2018)

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Article Thermodynamics

Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid

Xiaoshun Zhang et al.

ENERGY (2017)

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Article Construction & Building Technology

Generalized online transfer learning for climate control in residential buildings

Thomas Grubinger et al.

ENERGY AND BUILDINGS (2017)

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Article Computer Science, Artificial Intelligence

Online Transfer Learning with Multiple Homogeneous or Heterogeneous Sources

Qingyao Wu et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2017)

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Article Multidisciplinary Sciences

An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

David Murray et al.

SCIENTIFIC DATA (2017)

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Article Computer Science, Artificial Intelligence

Wind power prediction using deep neural network based meta regression and transfer learning

Aqsa Saeed Qureshi et al.

APPLIED SOFT COMPUTING (2017)

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Article Construction & Building Technology

Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning

Elena Mocanu et al.

ENERGY AND BUILDINGS (2016)

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Article Geochemistry & Geophysics

Domain Adaptation for the Classification of Remote Sensing Data An overview of recent advances

Devis Tuia et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2016)

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Article Construction & Building Technology

An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling

Ali Ghahramani et al.

BUILDING AND ENVIRONMENT (2015)

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Article Computer Science, Artificial Intelligence

Transfer learning using computational intelligence: A survey

Jie Lu et al.

KNOWLEDGE-BASED SYSTEMS (2015)

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Article Construction & Building Technology

On-line learning of indoor temperature forecasting models towards energy efficiency

F. Zamora-Martinez et al.

ENERGY AND BUILDINGS (2014)

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Article Construction & Building Technology

Utilizing artificial neural network to predict energy consumption and thermal comfort level: An indoor swimming pool case study

Bans Yuce et al.

ENERGY AND BUILDINGS (2014)

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Review Green & Sustainable Science & Technology

Assessing the energy efficiency improvement potentials of HVAC systems considering economic and environmental aspects at the hospitals

Ahmet Teke et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2014)

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Proceedings Paper Computer Science, Artificial Intelligence

Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective

Novi Patricia et al.

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2014)

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Article Computer Science, Artificial Intelligence

Transfer learning for activity recognition: a survey

Diane Cook et al.

KNOWLEDGE AND INFORMATION SYSTEMS (2013)

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Review Medicine, General & Internal

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

David Moher et al.

PLOS MEDICINE (2009)

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