# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
## 5 Digital Twin Technology for Energy Sector Development
### Algorithms and Deep Learning for Energy Efficiency
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **References** & **Year** & **Energy Simulation ML Technologies** & **Application** \\ \hline
6 & [117] & 2017 & Gradient Boosting, LR, SVM & Estimating commercial building energy consumption \\
7 & [118] & 2017 & OLR, Lasso, MARS, Bagging MARS, Boosting, SVM & Gas and electricity use prediction \\
8 & [119] & 2017 & A new integrated Markov model & Short term occupancy in commercial buildings prediction \\
9 & [120] & 2017 & NN, GP, EPR and EnergyPlus & Commercial buildings fuel consumption prediction \\
10 & [121] & 2017 & ELM, MLR, SVR, BPNN & Heating load prediction \\
11 & [122] & 2018 & GLM, ANN, SVM, RF, XGBoost & Data-driven building energy performance modeling \\
12 & [123] & 2018 & ELM, MLR, SVR, BPNN & Heating load prediction \\
13 & [124] & 2018 & RF, SVM & Prediction of EUI \\
14 & [125] & 2019 & Bayesian Optimization, Grid Search, ANN, GB, RF & Building energy systems modeling and optimization \\
15 & [126] & 2019 & SRV, CART, LR & Cooling loads prediction \\  & & RF, Lazy K-star, AMT, RDT, DPC, & Cooling loads prediction \\
16 & [127] & 2019 & M5 Rules, MLP, MPR, Function XNV, RBFR, MLR, SMOR, GPR, LMSR, Lazy & Heating and cooling load prediction \\  & & LWL, EN & \\
17 & [128] & 2019 & GBRT, SVM, NN & Building energy loads prediction \\
18 & [129] & 2019 & RF, MP, LLWR, AMT, EN, RBFR & Energy-efficient buildings heating load prediction \\
19 & [130] & 2019 & ANN & Multi-building energy consumption prediction \\
20 & [131] & 2019 & GRU, LSTM, S2S & Building energy consumption prediction \\
21 & [132] & 2019 & algorithm strategy for parameter optimization, JAVA & Building energy systems modeling and optimization \\  & & Decision Tree, Support Vector Machine & Building energy consumption prediction \\
22 & [133] & 2019 & Classifier, k-Nearest Neighbors Classifier, Innovative Clustering Techniques & Building energy systems optimization \\
23 & [134] & 2019 & Sensor Mesh Optimization & Weather data Prediction for building energy simulations \\
24 & [135] & 2020 & GCM & Building energy consumption prediction \\
25 & [136] & 2020 & RF & Short-term energy consumption prediction \\
26 & [137] & 2020 & BPNN, MLP... & Cooling energy consumption prediction \\
27 & [138] & 2020 & K mean, SVM & Building energy consumption prediction \\
28 & [139] & 2020 & RF, XGBoost & Building energy consumption prediction \\
29 & [140] & 2020 & GBRT & Non-domestic Building energy consumption prediction \\
30 & [141] & 2020 & SVM, RF, MLP, DNN, RNN, LSTM, GRU & R \& D building air-conditioning energy consumption prediction \\
31 & [142] & 2020 & LR, GBM & Building energy consumption prediction \\
32 & [143] & 2020 & ABC, LM, SVM, MARS, BMARS, RF, & Building energy models calibration \\  & & Bagging & Building energy consumption and BIPV \\
33 & [144] & 2020 & ANN, SVR, LSTM & electricity production prediction \\
34 & [145] & 2020 & GBR & Commercial building energy consumption prediction \\
35 & [146] & 2021 & ANN, SRV, Data Segmentation & Building energy consumption prediction \\
36 & [147] & 2021 & SVR, RF, GB, DNN & Building energy consumption prediction \\
37 & [148] & 2021 & Increment and Enrichment approach & Early-stage building energy consumption prediction \\
38 & [149] & 2021 & ANN and Monte Carlo & Energy consumption impacts analysis \\
39 & [150] & 2021 & CART, EBT, ANN, DNN & Cooling energy consumption prediction \\
40 & [151] & 2021 & PLS and Cubist Regression & Building energy consumption and carbon emissions prediction \\
41 & [152] & 2021 & XGBoost & Space cooling energy consumption prediction \\
42 & [153] & 2021 & LR, RR, SVR, DTR, RF, XGBoost & Public buildings energy consumption prediction \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._In the study of modern building energy management, machine learning algorithms are widely used to improve the accuracy of energy demand forecasting. In the single-building domain, [PERSON] [171] performed well using support vector machines (SVM) to predict energy consumption in industrial buildings.