Modeling and validation of a novel load model considering uncertain thermal disturbance in the district heating system

[1]  Xiao Chen,et al.  Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study , 2023, Energy.

[2]  Sisi Liu,et al.  Passive Sampling: A Greener Technique for the ‘Dual Carbon’ Goal While Implementing the Action Plan for Controlling Emerging Pollutants , 2022, Bulletin of Environmental Contamination and Toxicology.

[3]  B. Chalermsinsuwan,et al.  Optimizing utilization pathways for biomass to chemicals and energy by integrating emergy analysis and particle swarm optimization (PSO) , 2022, Renewable Energy.

[4]  N. Nikolopoulos,et al.  Dynamic investigation of centralized and decentralized storage systems for a district heating network , 2022, Journal of Energy Storage.

[5]  Xuan Zhou,et al.  Dynamic integrated control for Chinese district heating system to balance the heat supply and heat demand , 2022, Sustainable Cities and Society.

[6]  Thomas Ohlson Timoudas,et al.  A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries , 2022, Energy Conversion and Management.

[7]  S. Hamdaoui,et al.  Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods , 2022, Journal of Building Engineering.

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

[9]  Cagla Meral Akgül,et al.  Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey , 2022, Renewable and Sustainable Energy Reviews.

[10]  Yong Zhou,et al.  Application of Combined Models Based on Empirical Mode Decomposition, Deep Learning, and Autoregressive Integrated Moving Average Model for Short-Term Heating Load Predictions , 2022, Sustainability.

[11]  Jun Ma,et al.  Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model , 2022, Energy.

[12]  Mianshan Cui.  District heating load prediction algorithm based on bidirectional long short-term memory network model , 2022, Energy.

[13]  Shushu Zhu,et al.  Optimization Design and Performance Evaluation of a Hybrid Excitation Claw Pole Machine , 2022, Processes.

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

[15]  S. Karinka,et al.  Comparative energy analysis of a laboratory building with different materials using eQUEST simulation software , 2022, Materials Today: Proceedings.

[16]  G. Ciulla,et al.  An intelligent way to predict the building thermal needs: ANNs and optimization , 2021, Expert Syst. Appl..

[17]  Qiong Wu,et al.  Joint forecasting of multi-energy loads for a university based on copula theory and improved LSTM network , 2022, Energy Reports.

[18]  Xiu Zhao,et al.  Research on short-term load forecasting based on accumulated temperature effect and improved temporal convolutional network , 2022, Energy Reports.

[19]  S. Yoo,et al.  District Heater Load Forecasting Based on Machine Learning and Parallel CNN-LSTM Attention , 2022, SSRN Electronic Journal.

[20]  Yanfeng Liu,et al.  Comparison of machine-learning models for predicting short-term building heating load using operational parameters , 2021, Energy and Buildings.

[21]  Shenghui Wang,et al.  Heating load prediction based on particle swarm optimization support vector machine , 2021, Journal of Physics: Conference Series.

[22]  S. Leigh,et al.  Prediction of heating energy consumption with operation pattern variables for non-residential buildings using LSTM networks , 2021, Energy and Buildings.

[23]  G. Diarce,et al.  Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters , 2021, Energy.

[24]  Zhi Gao,et al.  Effect of urban form on microclimate and energy loads: Case study of generic residential district prototypes in Nanjing, China , 2021, Sustainable Cities and Society.

[25]  Guixiang Xue,et al.  Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model , 2021, Energy and Buildings.

[26]  Zhikun Ding,et al.  Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building , 2021 .

[27]  C. Balaras,et al.  A simple building energy model in form of an equivalent outdoor temperature , 2021 .

[28]  Zhongfu Li,et al.  Gauging the impacts of urbanization on CO2 emissions from the construction industry: Evidence from China. , 2021, Journal of environmental management.

[29]  Jiangyan Liu,et al.  Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification , 2021 .

[30]  Zhe Tian,et al.  A general transfer learning-based framework for thermal load prediction in regional energy system , 2021 .

[31]  Ruijun Feng,et al.  A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting , 2021, Inf. Sci..

[32]  N. Sakkas,et al.  Thermal Load Prediction of Communal District Heating Systems by Applying Data-Driven Machine Learning Methods , 2021, SSRN Electronic Journal.

[33]  Yanfeng Liu,et al.  Identifying the most significant input parameters for predicting district heating load using an association rule algorithm , 2020 .

[34]  Daniel Friedrich,et al.  Sensitivity analysis to reduce duplicated features in ANN training for district heat demand prediction , 2020, Energy and AI.

[35]  Firat Melih Yilmaz,et al.  Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting , 2020 .

[36]  Runming Yao,et al.  A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour , 2020, Energy.

[37]  Xiangfei Kong,et al.  Heating load prediction based on attention long short term memory: A case study of Xingtai , 2020 .

[38]  Shilei Lu,et al.  Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system , 2020 .

[39]  Huiqing Guo,et al.  Modeling heating demands in a Chinese-style solar greenhouse using the transient building energy simulation model TRNSYS , 2020 .

[40]  Chendong Wang,et al.  Study on refined control and prediction model of district heating station based on support vector machine , 2019 .

[41]  Qiang Zhang,et al.  Development and evaluation of cooling load prediction models for a factory workshop , 2019, Journal of Cleaner Production.

[42]  Xiaofeng Guo,et al.  Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.

[43]  Yakai Lu,et al.  Stepwise calibration for residential building thermal performance model using hourly heat consumption data , 2018, Energy and Buildings.

[44]  Bengt Sundén,et al.  Medium-term heat load prediction for an existing residential building based on a wireless on-off control system , 2018, Energy.

[45]  Andreas Sumper,et al.  A model for an economic evaluation of energy systems using TRNSYS , 2018 .

[46]  Chee Ming Lim,et al.  EnergyPlus models for the benchmarking of residential buildings in Brunei Darussalam , 2016, Energy and Buildings.

[47]  Qiang Zhang,et al.  Research on short-term and ultra-short-term cooling load prediction models for office buildings , 2017 .

[48]  Davy Geysen,et al.  Operational thermal load forecasting in district heating networks using machine learning and expert advice , 2017, ArXiv.

[49]  Magnus Dahl,et al.  Using ensemble weather predictions in district heating operation and load forecasting , 2017 .

[50]  Fredrik Wallin,et al.  An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks , 2017 .

[51]  Risto Lahdelma,et al.  Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system , 2016 .

[52]  M. Ozel.  Effect of indoor design temperature on the heating and cooling transmission loads , 2016 .

[53]  Mohammad Yusri Hassan,et al.  A review on applications of ANN and SVM for building electrical energy consumption forecasting , 2014 .

[54]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[55]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[56]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.