A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study
Abstract
:1. Introduction
2. Database
2.1. Building Feature Variables
2.2. Meta-Model Simulation in Batches
2.3. Database Establishment with MySQL
3. Methodology
3.1. Concept of Deep Learning
3.2. Task Design for Deep Learning
3.3. 1D-CNN Seq2Seq Model
3.4. Model Training and Evaluation
4. Results and Discussion
5. Case Study
5.1. District Building Loads Forecasting Framework
5.2. Load Forecasting Case for a Given District
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classification | Variable | Unit | Values |
---|---|---|---|
Building geometry | BSF | 0.15, 0.20, 0.30, 0.37 | |
Building envelope | WWR | 0.1, 0.4, 0.6, 0.8 | |
Design temperature | Ts | °C | 22, 24, 26, 28 |
Tw | °C | 16, 18, 20, 21 | |
Internal heat gain | Do | m2/p | 2, 5, 7, 10 (for office and hotel) |
10, 13, 16, 20 (for commercial) | |||
Dl | W/m2 | 2, 8, 14, 20 | |
De | W/m2 | 2, 8, 14, 20 |
Variable Sets | Building Loads | Variables |
---|---|---|
Cooling load | BSF, WWR, Ts, Do, Dl, and De. | |
Heating load | BSF, WWR, Tw, Do, Dl, and De. |
Structure | Network Layer | Layer Property | Dimension |
---|---|---|---|
Input | Input_var | InputLayer | (168, 12) |
Encoder | Conv1d_1 | Conv1D | (168, 128) |
Conv1d_2 | Conv1D | (168, 128) | |
Decoder | Conv1d_3 | Conv1D | (168, 64) |
Conv1d_4 | Conv1D | (168, 32) | |
Dropout Output | Dropout_1 | Dropout | (168, 32) |
Load | Dense | (168, 1) |
Building Type | LEHR | MAPE | ||
---|---|---|---|---|
Cooling Load | Heating Load | Cooling Load | Heating Load | |
Office | 91.1% | 89.4% | 4.4% | 3.5% |
Hotel | 90.3% | 89.9% | 3.7% | 5.9% |
Commercial | 92.2% | 90.9% | 7.2% | 4.1% |
No. | Type | BSF | WWR | Ts | Tw | Do | Dl | De |
---|---|---|---|---|---|---|---|---|
- | - | (°C) | (°C) | (m2/p) | (W/m2) | (W/m2) | ||
#1 | hotel | 0.16 | 0.4 | 24 | 20 | 10 | 10 | 13 |
#2 | office | 0.18 | 0.5 | 26 | 20 | 6 | 12 | 20 |
#3 | commercial | 0.21 | 0.5 | 26 | 20 | 3 | 15 | 20 |
Load Type | LEHR | MAPE |
---|---|---|
Cooling load | 100.0% | 3.7% |
Heating load | 93.1% | 6.7% |
Annual loads | 98.4% | 4.4% |
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Zhou, Y.; Liang, Y.; Pan, Y.; Yuan, X.; Xie, Y.; Jia, W. A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study. Buildings 2022, 12, 177. https://doi.org/10.3390/buildings12020177
Zhou Y, Liang Y, Pan Y, Yuan X, Xie Y, Jia W. A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study. Buildings. 2022; 12(2):177. https://doi.org/10.3390/buildings12020177
Chicago/Turabian StyleZhou, Yuhao, Yumin Liang, Yiqun Pan, Xiaolei Yuan, Yurong Xie, and Wenqi Jia. 2022. "A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study" Buildings 12, no. 2: 177. https://doi.org/10.3390/buildings12020177