# 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
[PERSON] [172] proposes a hybrid machine learning approach that considers human behavior and the interaction of building energy management systems to predict building energy consumption successfully. [PERSON] [170] presents a minimal sensing strategy for occupancy prediction using a comprehensive set of sensor data and deep learning methods, emphasizing the importance of indoor CO\({}_{2}\) levels and Wi-Fi-connected devices in predicting occupancy of different space types. The modelling model developed by [PERSON] [175] using the Light Gradient Boosting Machine \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **References** & **Year** & **Energy Simulation ML Technologies** & **Application** \\ \hline
43 & [154] & 2021 & MLP, RBFNN, GRNN, ELM, SVM, LS-SVM, GPR, RT, MT, RF, GBDT, XGBoost, LightGBM, CatBoost, MARS & Short-term building heating load prediction \\
44 & [155] & 2021 & MLR, RF, DNN & Integrated cooling tower systems performance prediction \\
45 & [156] & 2021 & Levenberg-Marquardt (LM) & Predictive control for energy-efficient building \\
46 & [157] & 2021 & LR, PLS, Lasso, MARS, Bagging MARS, SVM, Boosting, NN, RF, Cubist & Energy characteristics exploration \\
47 & [158] & 2021 & SVM, ANN... & Building energy consumption prediction \\
48 & [109] & 2021 & MOB algorithm & Analyee energy variables and energy consumption \\
49 & [159] & 2021 & ANN... & Life cycle assessment \\
50 & [160] & 2021 & MPC & Improve energy efficiency of HVAC \\
51 & [161] & 2021 & optimisation algorithm & Building energy performance analysis \\
52 & [162] & 2021 & K-means, Apriori Algorithm, Association rule learning & Improve building energy management \\
53 & [163] & 2021 & Q-learning & Building energy systems modeling and optimization \\
54 & [164] & 2022 & YOLOv4 & Improve energy efficiency of indoor lighting \\
55 & [165] & 2022 & ANN & Improve energy efficiency of HVAC \\
56 & [166] & 2022 & DNN, GD (Gradient descent) & Building energy consumption prediction \\
57 & [167] & 2022 & Optimization algorithm & Building energy systems optimization \\
58 & [168] & 2022 & ANN & Building energy consumption prediction \\
59 & [169] & 2022 & KNN, HNSW & Building energy consumption prediction \\
60 & [170] & 2022 & DNN, LSTM, LSTM (Bi-LSTM), GRU, GRU (Bi-LSTM) & Occupancy forecasting study \\
61 & [171] & 2023 & SVM & Industrial building energy consumption prediction \\
62 & [172] & 2023 & SGD, KNN, SVM, RF & Human-centred energy consumption prediction \\
63 & [173] & 2023 & DNN... & Building energy consumption prediction \\
64 & [174] & 2023 & Deep Q learning & Residential buildings energy systems controlling \\
65 & [175] & 2023 & LightGBM & Building energy consumption and GHG \\
66 & [176] & 2023 & RF, XGBoost, LightGBM, CatBoost & Cooling loads prediction \\
67 & [177] & 2023 & ANN, ANFIS & Building energy systems controlling \\
68 & [178] & 2023 & RNN, LSTM & Human activity recognition for energy saving \\
69 & [108] & 2023 & MLPNN & Multi-energy load prediction \\
70 & [179] & 2023 & MPC & Model predictive control \\
71 & [180] & 2023 & RDPG, A3C, LSTM, CNN, ANN, AVR & Multi building performance vectors prediction \\
72 & [181] & 2023 & RF, SVM, KNN & Behavior-orientated prediction \\
73 & [182] & 2023 & LSTM & Building energy consumption prediction \\
74 & [183] & 2023 & AR, ARIMA, LSTM, CNN & Electricity load and price prediction \\
75 & [184] & 2023 & ANN, SVMs, FM, DTs, KNN & Building energy modeling and \\
76 & [185] & 2023 & ANN, DT, SVM & Building energy consumption prediction \\
77 & [186] & 2023 & DNN, LSTM, GRU & Building occupancy prediction \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._combined with the Shapley Additive exPlanation algorithm can predict the energy use and greenhouse gas emissions of residential buildings and identify the most influential variables. [PERSON]'s [108] multi-layer perceptron neural network can effectively predict the multi-energy load of buildings and found that the optimization algorithm impacts the prediction effect.