Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network
Abstract
:1. Introduction
- We proposed a new hybrid neural network model for real-world building energy consumption forecasting based on SSA. Compared with traditional forecasting models, the proposed model achieved the highest prediction accuracy and had stronger peak and valley capture ability, which effectively alleviated the lag of extreme point data forecasting;
- The simulation results demonstrated that the proposed model still had excellent forecasting precision and stability in the multi-step ahead forecasting scenario, meeting the basic building energy consumption forecasting requirements;
- We compared and analyzed the forecasting effects of neural network models optimized by five decomposition algorithms in the multi-step ahead forecasting scenario. The simulation results showed that the SSA method was a suitable feature extractor that reduced the computational burden and improved the forecast accuracy of the model.
2. Related Work
3. Methodology
3.1. Singular Spectrum Analysis
3.2. Convolutional Neural Network
3.3. Bidirectional Gated Neural Network
3.4. Multi-Step Forecasting Strategy
4. The Hybrid Multi-Step Forecast Model
4.1. The Framework of the Proposed Model
4.2. SSA Data Preprocessing
4.3. Experimental Environment and Network Hyperparameter Setting
5. Case Studies and Results
5.1. Comparison of Direct Forecast Results through Different Models
5.2. Comparison of Forecast Results of Different Models under Singular Spectrum Decomposition
5.3. Comparison of Forecast Results under Different Decomposition Algorithms
6. Conclusions and Future Works
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Index | Definition | Formula |
---|---|---|
Mean absolute error | ||
Root mean square error | ||
Mean absolute percentage error | ||
Coefficient of determination | ||
Promoting percentages of mean absolute error | ||
Promoting percentages of root mean square error | ||
Promoting percentages of mean absolute percentage error |
Consumption Type | Statistic Indices | |||||
---|---|---|---|---|---|---|
Mean (kWh) | Max (kWh) | Min (kWh) | Std | Skew. (Skewness) | Kurt. (Kurtosis) | |
Electricity | 1601.19 | 4700.72 | 717.98 | 774.77 | 1.10 | 0.23 |
Gas | 3204.10 | 19,084.96 | 269.42 | 3567.91 | 2.04 | 3.76 |
Layer Type | Hyperparameter Configuration |
---|---|
Conv1D | Filters: 16 kernel size: 3 activation: Relu padding: same |
Max-pooling | Pool size: 2 stride: 1 padding: same |
Conv1D | Filters: 32 kernel size: 3 activation: Relu padding: same |
Max-pooling | Pool size: 2 stride: 1 padding: same |
Dense | Hidden node: 32 activation: Relu |
BiGRU | Hidden node: 64 activation: tanh |
Dense | Hidden node: 32 activation: Relu |
Dense | Hidden node: 1/2/4/6 activation: linear |
Types | Models | MAE (kWh) | RMSE (kWh) | MAPE (%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1-Step | 2-Step | 4-Step | 6-Step | 1-Step | 2-Step | 4-Step | 6-Step | 1-Step | 2-Step | 4-Step | 6-Step | ||
Electricity consumption | LR | 120.06 | 145.24 | 171.62 | 198.26 | 210.62 | 246.06 | 288.81 | 338.95 | 5.74 | 6.96 | 8.18 | 9.77 |
MLP | 89.00 | 119.48 | 150.14 | 170.78 | 149.55 | 188.62 | 232.99 | 266.02 | 4.62 | 5.75 | 7.48 | 8.43 | |
CNN | 101.11 | 122.21 | 178.00 | 175.30 | 164.75 | 201.43 | 278.31 | 276.53 | 5.24 | 6.22 | 8.27 | 9.25 | |
GRU | 87.00 | 121.16 | 153.83 | 172.19 | 153.54 | 197.55 | 242.81 | 268.86 | 4.46 | 6.10 | 7.33 | 8.29 | |
BiGRU | 81.51 | 105.39 | 140.12 | 159.43 | 144.28 | 175.18 | 219.34 | 253.77 | 4.26 | 5.32 | 7.25 | 8.17 | |
CNNGRU | 81.71 | 111.69 | 134.05 | 160.28 | 141.51 | 183.40 | 214.67 | 257.08 | 4.04 | 5.53 | 6.62 | 7.77 | |
CNNBiGRU | 70.06 | 91.35 | 116.35 | 139.09 | 123.82 | 151.38 | 178.67 | 214.92 | 3.55 | 4.65 | 6.20 | 7.41 | |
Gas consumption | LR | 413.44 | 492.57 | 577.35 | 661.04 | 694.47 | 856.17 | 987.44 | 1151.58 | 12.53 | 14.40 | 16.92 | 19.36 |
MLP | 358.77 | 456.35 | 477.81 | 532.61 | 531.03 | 653.25 | 708.74 | 815.10 | 12.59 | 13.38 | 14.70 | 16.31 | |
CNN | 358.09 | 482.50 | 474.26 | 525.21 | 526.86 | 702.83 | 688.95 | 793.38 | 13.90 | 16.40 | 16.93 | 17.44 | |
GRU | 363.33 | 448.09 | 505.15 | 529.22 | 522.63 | 716.50 | 800.26 | 837.83 | 11.69 | 12.64 | 14.13 | 15.19 | |
BiGRU | 334.96 | 391.52 | 425.03 | 467.70 | 509.77 | 647.71 | 670.21 | 735.65 | 10.37 | 11.70 | 13.40 | 14.95 | |
CNNGRU | 301.79 | 399.83 | 437.00 | 490.90 | 468.57 | 619.00 | 693.82 | 777.07 | 10.12 | 13.31 | 13.82 | 14.42 | |
CNNBiGRU | 272.20 | 326.78 | 386.46 | 460.75 | 449.99 | 515.56 | 614.03 | 718.12 | 8.33 | 9..99 | 11.25 | 13.72 |
Types | Models | MAE (kWh) | RMSE (kWh) | MAPE (%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1-Step | 2-Step | 4-Step | 6-Step | 1-Step | 2-Step | 4-Step | 6-Step | 1-Step | 2-Step | 4-Step | 6-Step | ||
Electricity consumption | SSA-CNN | 37.69 | 56.95 | 82.71 | 94.41 | 60.48 | 87.41 | 134.18 | 152.30 | 2.05 | 3.10 | 4.00 | 4.71 |
SSA-GRU | 34.04 | 54.12 | 75.16 | 99.34 | 60.49 | 98.91 | 130.94 | 149.43 | 1.71 | 2.67 | 3.75 | 5.02 | |
SSA-BiGRU | 28.57 | 44.84 | 63.69 | 85.28 | 53.78 | 83.89 | 114.29 | 134.62 | 1.39 | 2.28 | 3.18 | 4.35 | |
SSA-CNNGRU | 27.87 | 37.36 | 47.17 | 79.78 | 50.16 | 70.36 | 81.32 | 122.05 | 1.27 | 1.68 | 2.33 | 3.97 | |
SSA-CNNBiGRU | 18.52 | 25.41 | 38.87 | 50.96 | 38.01 | 49.05 | 64.41 | 74.86 | 0.86 | 1.21 | 1.94 | 2.72 | |
Gas consumption | SSA-CNN | 106.34 | 136.73 | 184.41 | 234.30 | 149.51 | 186.67 | 260.81 | 330.70 | 3.61 | 4.85 | 6.59 | 8.19 |
SSA-GRU | 103.08 | 135.29 | 193.23 | 237.25 | 140.02 | 196.97 | 255.59 | 326.18 | 3.25 | 4.63 | 6.24 | 7.88 | |
SSA-BiGRU | 92.37 | 115.18 | 152.40 | 227.28 | 130.57 | 160.13 | 225.64 | 295.99 | 2.85 | 3.57 | 4.68 | 6.85 | |
SSA-CNNGRU | 84.97 | 98.23 | 135.15 | 183.68 | 124.59 | 145.89 | 212.55 | 278.61 | 2.32 | 3.07 | 4.29 | 6.04 | |
SSA-CNNBiGRU | 60.71 | 78.02 | 119.33 | 172.31 | 93.52 | 115.36 | 174.24 | 247.75 | 1.78 | 2.44 | 3.86 | 5.67 |
Models | Electricity Consumption | Gas Consumption | ||||
---|---|---|---|---|---|---|
/% | /% | /% | /% | /% | /% | |
CNN | 62.72 | 63.29 | 60.88 | 70.30 | 71.22 | 74.03 |
GRU | 60.87 | 60.60 | 61.66 | 71.63 | 73.21 | 72.20 |
BiGRU | 64.95 | 62.73 | 67.37 | 72.42 | 74.39 | 72.51 |
CNNGRU | 65.89 | 64.55 | 68.56 | 71.84 | 73.41 | 77.07 |
CNNBiGRU | 76.85 | 69.30 | 75.77 | 77.70 | 79.22 | 78.63 |
Types | Models | MAE (kWh) | RMSE (kWh) | MAPE (%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1-Step | 2-Step | 4-Step | 6-Step | 1-Step | 2-Step | 4-Step | 6-Step | 1-Step | 2-Step | 4-Step | 6-Step | ||
Electricity consumption | EMD-CNNBiGRU | 51.76 | 59.30 | 73.55 | 98.33 | 78.97 | 92.04 | 114.78 | 157.24 | 2.85 | 3.16 | 4.00 | 5.43 |
EEMD-CNNBiGRU | 40.25 | 50.33 | 63.12 | 81.60 | 54.31 | 68.74 | 87.30 | 111.75 | 2.26 | 3.04 | 3.75 | 5.00 | |
EWT-CNNBiGRU | 29.03 | 42.33 | 50.59 | 64.45 | 43.45 | 58.10 | 73.83 | 92.39 | 1.46 | 2.29 | 2.63 | 3.44 | |
VMD-CNNBiGRU | 35.84 | 42.70 | 56.66 | 69.83 | 55.46 | 68.00 | 87.49 | 103.14 | 1.88 | 2.24 | 2.96 | 3.69 | |
SSA-CNNBiGRU | 18.52 | 25.41 | 38.87 | 50.96 | 38.01 | 49.05 | 64.41 | 74.86 | 0.86 | 1.21 | 1.94 | 2.72 | |
Gas consumption | EMD-CNNBiGRU | 139.77 | 186.33 | 254.38 | 310.73 | 204.47 | 278.29 | 381.10 | 443.92 | 5.04 | 6.67 | 9.37 | 11.83 |
EEMD-CNNBiGRU | 118.56 | 127.31 | 181.13 | 236.61 | 159.43 | 175.42 | 258.87 | 332.68 | 4.45 | 4.82 | 6.60 | 8.82 | |
EWT-CNNBiGRU | 76.84 | 91.44 | 135.77 | 180.54 | 105.79 | 122.88 | 187.76 | 252.64 | 2.98 | 3.51 | 5.05 | 6.71 | |
VMD-CNNBiGRU | 103.94 | 111.57 | 158.78 | 190.49 | 142.72 | 156.61 | 219.46 | 268.57 | 3.80 | 4.10 | 5.88 | 6.79 | |
SSA-CNNBiGRU | 60.71 | 78.02 | 119..33 | 172.31 | 93.52 | 115.36 | 174.24 | 247.75 | 1.78 | 2.44 | 3.86 | 5.67 |
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Wei, S.; Bai, X. Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network. Energies 2022, 15, 1743. https://doi.org/10.3390/en15051743
Wei S, Bai X. Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network. Energies. 2022; 15(5):1743. https://doi.org/10.3390/en15051743
Chicago/Turabian StyleWei, Shangfu, and Xiaoqing Bai. 2022. "Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network" Energies 15, no. 5: 1743. https://doi.org/10.3390/en15051743