An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting †
Abstract
:1. Introduction
- We conducted multistep-ahead forecasting for the hourly power consumption of buildings to adequately cope with sudden changes in power consumption caused by various unexpected events, such as peak and blackout, instead of day-ahead point STLF.
- We constructed an attention-based multilayered GRU model to achieve faster and more stable multistep-ahead STLF than other deep learning (DL) architectures.
- We verified the superiority of the proposed model through extensive comparisons with several state-of-the-art forecasting models using the power consumption data of three office buildings.
2. Related Studies
3. Data Preprocessing
3.1. Data Collection
3.2. Feature Extraction
3.3. Correlation Analysis
4. Forecasting Model Construction
4.1. Gated Recurrent Unit Model
4.2. Attention Mechanism
5. Experimental Results
5.1. Experimental Design
5.2. Experimental Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Points | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
1 | 17.44 | 14.70 | 23.21 | 13.56 | 15.67 | 14.89 | 13.58 | 13.54 | 13.41 |
2 | 17.43 | 15.44 | 23.39 | 13.67 | 14.89 | 14.27 | 13.51 | 13.58 | 13.44 |
3 | 17.48 | 15.87 | 23.65 | 13.77 | 15.39 | 14.34 | 13.89 | 14.39 | 13.49 |
4 | 17.59 | 16.37 | 23.92 | 13.85 | 15.55 | 14.33 | 13.70 | 14.45 | 13.51 |
5 | 17.57 | 16.37 | 24.19 | 13.91 | 15.82 | 14.40 | 13.74 | 14.42 | 13.52 |
6 | 17.70 | 17.03 | 24.49 | 13.96 | 15.98 | 14.49 | 13.81 | 14.40 | 13.61 |
7 | 17.90 | 17.03 | 24.67 | 14.01 | 15.90 | 14.58 | 13.78 | 14.34 | 13.59 |
8 | 18.03 | 17.03 | 24.89 | 14.04 | 15.84 | 14.66 | 13.73 | 14.25 | 13.62 |
9 | 18.03 | 16.85 | 25.09 | 14.07 | 15.83 | 14.76 | 13.67 | 14.19 | 13.64 |
10 | 18.27 | 17.24 | 25.26 | 14.10 | 15.88 | 14.76 | 13.66 | 14.16 | 13.64 |
11 | 18.35 | 17.19 | 25.43 | 14.11 | 15.93 | 14.94 | 13.67 | 14.30 | 13.64 |
12 | 18.58 | 17.19 | 25.54 | 14.13 | 15.95 | 14.96 | 13.68 | 14.11 | 13.64 |
13 | 18.67 | 17.40 | 25.50 | 14.14 | 15.96 | 14.99 | 13.69 | 14.10 | 13.65 |
14 | 18.47 | 17.29 | 25.43 | 14.16 | 15.96 | 15.03 | 13.70 | 14.12 | 13.65 |
15 | 18.40 | 17.17 | 25.30 | 14.16 | 15.96 | 15.06 | 13.73 | 14.14 | 13.67 |
16 | 18.40 | 17.08 | 25.10 | 14.17 | 15.99 | 15.09 | 13.75 | 14.15 | 13.67 |
17 | 18.42 | 17.41 | 24.96 | 14.18 | 16.01 | 15.10 | 13.79 | 14.18 | 13.68 |
18 | 18.49 | 17.01 | 24.76 | 14.19 | 16.02 | 15.11 | 13.83 | 14.20 | 13.68 |
19 | 18.46 | 17.16 | 24.48 | 14.20 | 16.02 | 15.11 | 13.85 | 14.20 | 13.69 |
20 | 18.42 | 17.22 | 24.30 | 14.22 | 16.03 | 15.10 | 13.89 | 14.20 | 13.71 |
21 | 18.35 | 17.21 | 24.14 | 14.24 | 16.02 | 15.09 | 13.91 | 14.22 | 13.72 |
22 | 18.75 | 17.28 | 24.02 | 14.26 | 16.03 | 15.09 | 13.93 | 14.23 | 13.73 |
23 | 19.11 | 16.98 | 23.92 | 14.28 | 16.04 | 15.07 | 13.98 | 14.23 | 13.76 |
24 | 19.84 | 17.45 | 23.88 | 14.31 | 16.06 | 15.06 | 14.01 | 14.24 | 13.78 |
Avg. | 18.26 | 16.87 | 24.56 | 14.07 | 15.86 | 14.85 | 13.77 | 14.18 | 13.63 |
Points | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
1 | 20.62 | 15.96 | 29.23 | 11.82 | 18.89 | 12.10 | 11.91 | 11.53 | 11.56 |
2 | 21.51 | 16.67 | 29.40 | 11.91 | 19.44 | 12.10 | 11.94 | 12.88 | 11.73 |
3 | 22.16 | 16.79 | 29.67 | 11.98 | 21.39 | 12.73 | 11.96 | 12.93 | 11.74 |
4 | 22.83 | 17.15 | 29.94 | 12.04 | 22.99 | 13.06 | 11.97 | 13.62 | 11.75 |
5 | 23.37 | 18.42 | 30.15 | 12.08 | 23.72 | 13.25 | 11.97 | 13.55 | 11.77 |
6 | 23.87 | 18.69 | 30.40 | 12.11 | 23.94 | 13.30 | 11.98 | 13.83 | 11.80 |
7 | 24.14 | 18.29 | 30.65 | 12.14 | 24.03 | 13.26 | 11.98 | 13.74 | 11.82 |
8 | 24.67 | 18.77 | 30.87 | 12.16 | 24.12 | 13.22 | 11.99 | 13.76 | 11.82 |
9 | 25.19 | 19.78 | 31.09 | 12.17 | 24.22 | 13.19 | 11.99 | 13.68 | 11.82 |
10 | 25.35 | 19.60 | 31.34 | 12.18 | 24.28 | 13.17 | 12.00 | 13.62 | 11.83 |
11 | 25.53 | 20.68 | 31.57 | 12.19 | 24.33 | 13.18 | 12.00 | 13.59 | 11.83 |
12 | 25.71 | 20.43 | 31.79 | 12.20 | 24.34 | 13.18 | 12.00 | 13.57 | 11.83 |
13 | 25.60 | 20.80 | 31.93 | 12.20 | 24.32 | 13.20 | 12.01 | 13.56 | 11.85 |
14 | 24.84 | 20.64 | 31.98 | 12.20 | 24.26 | 13.20 | 12.01 | 13.49 | 11.84 |
15 | 24.43 | 20.18 | 31.99 | 12.20 | 24.22 | 13.19 | 12.01 | 13.42 | 11.84 |
16 | 24.18 | 19.59 | 31.94 | 12.20 | 24.17 | 13.18 | 12.02 | 13.37 | 11.85 |
17 | 24.01 | 19.76 | 31.85 | 12.21 | 24.15 | 13.16 | 12.02 | 13.34 | 11.85 |
18 | 23.58 | 19.15 | 31.73 | 12.21 | 24.16 | 13.16 | 12.02 | 13.35 | 11.86 |
19 | 23.57 | 19.23 | 31.58 | 12.22 | 24.16 | 13.15 | 12.02 | 13.36 | 11.86 |
20 | 23.27 | 19.62 | 31.42 | 12.23 | 24.17 | 13.14 | 12.03 | 13.34 | 11.87 |
21 | 23.14 | 19.72 | 31.29 | 12.25 | 24.18 | 13.11 | 12.03 | 13.34 | 11.88 |
22 | 23.18 | 19.67 | 31.15 | 12.27 | 24.21 | 13.17 | 12.03 | 13.33 | 11.88 |
23 | 23.75 | 20.10 | 30.99 | 12.29 | 24.23 | 13.21 | 12.03 | 13.31 | 11.88 |
24 | 24.63 | 20.05 | 30.82 | 12.31 | 24.26 | 13.26 | 12.03 | 13.30 | 11.88 |
Avg. | 23.88 | 19.16 | 31.03 | 12.16 | 23.59 | 13.08 | 12.00 | 13.37 | 11.81 |
Points | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
1 | 6.32 | 7.76 | 8.80 | 6.31 | 6.42 | 6.69 | 6.27 | 6.68 | 5.91 |
2 | 6.37 | 7.25 | 8.82 | 6.37 | 7.02 | 6.69 | 6.27 | 6.67 | 6.01 |
3 | 6.44 | 7.60 | 8.87 | 6.41 | 7.13 | 6.67 | 6.29 | 6.67 | 6.03 |
4 | 6.51 | 7.84 | 8.90 | 6.45 | 7.19 | 6.79 | 6.28 | 6.83 | 6.05 |
5 | 6.58 | 7.77 | 8.93 | 6.48 | 7.21 | 6.73 | 6.29 | 6.81 | 6.07 |
6 | 6.65 | 7.86 | 8.98 | 6.51 | 7.23 | 6.76 | 6.28 | 6.86 | 6.07 |
7 | 6.67 | 7.93 | 9.04 | 6.53 | 7.26 | 6.74 | 6.28 | 6.84 | 6.08 |
8 | 6.68 | 8.17 | 9.09 | 6.55 | 7.25 | 6.74 | 6.28 | 6.81 | 6.09 |
9 | 6.69 | 8.12 | 9.14 | 6.56 | 7.27 | 6.75 | 6.29 | 6.78 | 6.09 |
10 | 6.77 | 8.31 | 9.21 | 6.57 | 7.29 | 6.77 | 6.29 | 6.78 | 6.09 |
11 | 6.80 | 8.46 | 9.26 | 6.57 | 7.31 | 6.80 | 6.29 | 6.78 | 6.09 |
12 | 6.80 | 8.55 | 9.30 | 6.58 | 7.33 | 6.84 | 6.29 | 6.78 | 6.10 |
13 | 6.77 | 8.45 | 9.31 | 6.58 | 7.34 | 6.86 | 6.29 | 6.79 | 6.10 |
14 | 6.78 | 8.59 | 9.30 | 6.58 | 7.34 | 6.87 | 6.29 | 6.79 | 6.10 |
15 | 6.78 | 8.72 | 9.28 | 6.58 | 7.35 | 6.89 | 6.30 | 6.80 | 6.10 |
16 | 6.81 | 8.77 | 9.27 | 6.59 | 7.35 | 6.91 | 6.30 | 6.81 | 6.11 |
17 | 6.79 | 8.49 | 9.23 | 6.60 | 7.36 | 6.93 | 6.30 | 6.81 | 6.11 |
18 | 6.83 | 8.63 | 9.19 | 6.61 | 7.36 | 6.95 | 6.30 | 6.81 | 6.13 |
19 | 6.92 | 8.63 | 9.16 | 6.62 | 7.37 | 6.95 | 6.30 | 6.81 | 6.12 |
20 | 7.00 | 8.68 | 9.12 | 6.63 | 7.38 | 6.97 | 6.30 | 6.82 | 6.12 |
21 | 7.10 | 8.35 | 9.07 | 6.64 | 7.40 | 6.97 | 6.30 | 6.84 | 6.12 |
22 | 7.24 | 8.72 | 9.02 | 6.66 | 7.40 | 6.97 | 6.30 | 6.85 | 6.12 |
23 | 7.50 | 8.69 | 8.98 | 6.67 | 7.40 | 6.98 | 6.30 | 6.85 | 6.12 |
24 | 7.84 | 8.82 | 8.94 | 6.69 | 7.39 | 7.00 | 6.30 | 6.85 | 6.12 |
Avg. | 6.82 | 8.30 | 9.09 | 6.56 | 7.26 | 6.85 | 6.29 | 6.80 | 6.09 |
Points | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
1 | 27.40 | 22.34 | 37.85 | 22.80 | 24.97 | 25.56 | 22.93 | 23.56 | 22.44 |
2 | 27.59 | 23.76 | 38.56 | 23.01 | 23.69 | 25.53 | 22.49 | 23.61 | 22.46 |
3 | 27.90 | 24.53 | 39.41 | 23.15 | 23.30 | 25.85 | 22.32 | 23.69 | 22.49 |
4 | 28.10 | 24.96 | 40.38 | 23.25 | 23.28 | 25.69 | 22.36 | 23.73 | 22.50 |
5 | 28.13 | 25.42 | 41.69 | 23.33 | 23.52 | 25.86 | 22.62 | 23.86 | 22.60 |
6 | 28.22 | 25.70 | 43.25 | 23.40 | 23.73 | 26.16 | 22.78 | 23.90 | 22.62 |
7 | 28.27 | 25.75 | 44.17 | 23.45 | 23.67 | 26.39 | 22.96 | 24.00 | 22.63 |
8 | 28.36 | 25.53 | 45.96 | 23.50 | 23.61 | 26.56 | 23.06 | 24.06 | 22.80 |
9 | 28.30 | 25.59 | 47.51 | 23.54 | 23.59 | 26.73 | 23.10 | 24.15 | 23.06 |
10 | 28.52 | 26.09 | 47.91 | 23.56 | 23.62 | 26.85 | 23.17 | 24.25 | 23.16 |
11 | 28.56 | 25.83 | 48.28 | 23.59 | 23.66 | 26.95 | 23.22 | 24.38 | 23.16 |
12 | 28.85 | 26.22 | 48.22 | 23.61 | 23.68 | 27.03 | 23.32 | 24.41 | 23.31 |
13 | 28.86 | 26.12 | 48.03 | 23.62 | 23.68 | 27.12 | 23.31 | 24.41 | 23.22 |
14 | 28.66 | 26.09 | 48.00 | 23.63 | 23.71 | 27.21 | 23.34 | 24.45 | 23.31 |
15 | 28.56 | 25.84 | 47.74 | 23.63 | 23.75 | 27.26 | 23.40 | 24.50 | 23.35 |
16 | 28.56 | 25.70 | 46.77 | 23.63 | 23.77 | 27.29 | 23.43 | 24.54 | 23.36 |
17 | 28.42 | 26.06 | 45.74 | 23.63 | 23.80 | 27.30 | 23.47 | 24.56 | 23.39 |
18 | 28.48 | 25.62 | 44.54 | 23.63 | 23.82 | 27.30 | 23.51 | 24.57 | 23.38 |
19 | 28.45 | 25.61 | 42.85 | 23.63 | 23.80 | 27.30 | 23.51 | 24.58 | 23.39 |
20 | 28.33 | 25.75 | 42.03 | 23.64 | 23.78 | 27.29 | 23.53 | 24.55 | 23.52 |
21 | 28.41 | 25.72 | 41.38 | 23.65 | 23.76 | 27.36 | 23.54 | 24.56 | 23.50 |
22 | 28.90 | 25.84 | 40.79 | 23.67 | 23.76 | 27.41 | 23.54 | 24.56 | 23.55 |
23 | 29.56 | 25.74 | 40.11 | 23.68 | 23.74 | 27.55 | 23.56 | 24.53 | 23.63 |
24 | 30.46 | 26.13 | 39.48 | 23.69 | 23.76 | 27.62 | 23.53 | 24.49 | 23.65 |
Avg. | 28.49 | 25.50 | 43.78 | 23.50 | 23.73 | 26.80 | 23.17 | 24.25 | 22.44 |
Points | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
1 | 24.03 | 19.28 | 34.54 | 19.09 | 21.21 | 22.03 | 19.21 | 20.88 | 19.11 |
2 | 23.83 | 19.99 | 35.02 | 19.24 | 20.04 | 21.82 | 19.26 | 21.69 | 19.21 |
3 | 23.83 | 20.57 | 35.54 | 19.33 | 21.44 | 22.22 | 19.29 | 21.43 | 19.27 |
4 | 23.83 | 21.16 | 36.12 | 19.41 | 22.81 | 22.42 | 19.30 | 21.88 | 19.26 |
5 | 23.88 | 21.60 | 36.63 | 19.47 | 23.48 | 22.52 | 19.36 | 21.85 | 19.33 |
6 | 23.86 | 22.00 | 37.26 | 19.51 | 23.61 | 22.58 | 19.41 | 22.14 | 19.39 |
7 | 24.00 | 21.96 | 37.95 | 19.55 | 23.69 | 22.60 | 19.44 | 22.17 | 19.42 |
8 | 24.08 | 22.16 | 38.68 | 19.58 | 23.75 | 22.58 | 19.51 | 22.18 | 19.45 |
9 | 24.06 | 22.62 | 39.17 | 19.60 | 23.86 | 22.59 | 19.52 | 22.15 | 19.49 |
10 | 24.04 | 22.70 | 39.84 | 19.62 | 23.99 | 22.62 | 19.53 | 22.10 | 19.47 |
11 | 23.91 | 23.20 | 40.43 | 19.64 | 24.10 | 22.63 | 19.53 | 22.13 | 19.48 |
12 | 24.13 | 22.97 | 40.84 | 19.65 | 24.13 | 22.65 | 19.54 | 22.13 | 19.46 |
13 | 24.13 | 23.35 | 41.09 | 19.66 | 24.13 | 22.69 | 19.55 | 22.07 | 19.53 |
14 | 23.91 | 23.58 | 41.08 | 19.66 | 24.09 | 22.72 | 19.57 | 22.05 | 19.49 |
15 | 23.85 | 23.27 | 40.91 | 19.66 | 24.07 | 22.75 | 19.57 | 22.00 | 19.51 |
16 | 23.69 | 22.79 | 40.70 | 19.66 | 24.03 | 22.77 | 19.60 | 22.00 | 19.53 |
17 | 23.61 | 23.12 | 40.34 | 19.66 | 24.00 | 22.79 | 19.62 | 22.04 | 19.60 |
18 | 23.64 | 22.65 | 39.93 | 19.65 | 24.00 | 22.80 | 19.61 | 22.06 | 19.59 |
19 | 23.69 | 22.72 | 39.43 | 19.66 | 24.00 | 22.80 | 19.62 | 22.08 | 19.59 |
20 | 23.75 | 22.91 | 38.90 | 19.67 | 23.99 | 22.79 | 19.63 | 22.08 | 19.56 |
21 | 23.87 | 22.96 | 38.39 | 19.69 | 23.99 | 22.78 | 19.65 | 22.09 | 19.59 |
22 | 24.04 | 22.91 | 37.82 | 19.71 | 23.99 | 22.72 | 19.66 | 22.09 | 19.57 |
23 | 24.70 | 23.14 | 37.26 | 19.75 | 24.00 | 22.69 | 19.65 | 22.08 | 19.61 |
24 | 25.51 | 23.10 | 36.80 | 19.77 | 24.00 | 22.65 | 19.66 | 22.11 | 19.58 |
Avg. | 23.99 | 22.36 | 38.53 | 19.58 | 23.52 | 22.59 | 19.51 | 21.98 | 19.46 |
Points | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
1 | 11.40 | 11.02 | 16.62 | 10.24 | 10.96 | 11.56 | 10.16 | 11.29 | 10.09 |
2 | 11.46 | 11.19 | 16.66 | 10.37 | 11.21 | 11.86 | 10.37 | 11.10 | 10.08 |
3 | 11.60 | 11.86 | 16.68 | 10.47 | 11.62 | 11.69 | 10.32 | 11.24 | 10.25 |
4 | 11.66 | 11.84 | 16.66 | 10.55 | 11.85 | 12.02 | 10.35 | 11.46 | 10.46 |
5 | 11.75 | 12.12 | 16.64 | 10.61 | 11.87 | 11.99 | 10.56 | 11.26 | 10.33 |
6 | 11.82 | 12.38 | 16.65 | 10.66 | 12.25 | 12.16 | 10.51 | 11.42 | 10.52 |
7 | 11.76 | 12.36 | 16.75 | 10.71 | 12.42 | 12.22 | 10.55 | 11.52 | 10.54 |
8 | 11.74 | 12.39 | 16.87 | 10.74 | 12.52 | 12.29 | 10.72 | 11.60 | 10.57 |
9 | 11.67 | 12.63 | 17.02 | 10.76 | 12.61 | 12.36 | 10.71 | 11.63 | 10.63 |
10 | 11.83 | 12.88 | 17.18 | 10.77 | 12.70 | 12.40 | 10.67 | 11.67 | 10.61 |
11 | 11.87 | 12.68 | 17.32 | 10.77 | 12.76 | 12.43 | 10.75 | 11.68 | 10.74 |
12 | 11.85 | 12.88 | 17.46 | 10.78 | 12.80 | 12.48 | 10.71 | 11.67 | 10.77 |
13 | 11.64 | 12.97 | 17.50 | 10.78 | 12.86 | 12.51 | 10.62 | 11.66 | 10.67 |
14 | 11.58 | 12.66 | 17.46 | 10.78 | 12.87 | 12.53 | 10.65 | 11.67 | 10.59 |
15 | 11.57 | 12.80 | 17.42 | 10.79 | 12.89 | 12.56 | 10.77 | 11.69 | 10.58 |
16 | 11.57 | 12.81 | 17.32 | 10.79 | 12.90 | 12.57 | 10.75 | 11.70 | 10.56 |
17 | 11.43 | 12.86 | 17.22 | 10.80 | 12.89 | 12.58 | 10.77 | 11.69 | 10.74 |
18 | 11.46 | 12.86 | 17.20 | 10.81 | 12.88 | 12.60 | 10.81 | 11.67 | 10.51 |
19 | 11.69 | 12.94 | 17.18 | 10.82 | 12.89 | 12.62 | 10.78 | 11.65 | 10.66 |
20 | 11.95 | 12.84 | 17.13 | 10.84 | 12.91 | 12.64 | 10.66 | 11.65 | 10.70 |
21 | 12.18 | 12.39 | 16.99 | 10.86 | 12.93 | 12.64 | 10.82 | 11.66 | 10.80 |
22 | 12.30 | 12.82 | 16.79 | 10.88 | 12.94 | 12.64 | 10.71 | 11.66 | 10.88 |
23 | 12.90 | 12.63 | 16.56 | 10.91 | 12.96 | 12.64 | 10.84 | 11.65 | 10.67 |
24 | 13.73 | 13.08 | 16.38 | 10.94 | 12.97 | 12.65 | 10.76 | 11.65 | 10.84 |
Avg. | 11.85 | 12.50 | 16.99 | 10.73 | 12.52 | 12.36 | 10.64 | 11.56 | 10.57 |
References
- Desai, S.; Alhadad, R.; Mahmood, A.; Chilamkurti, N.; Rho, S. Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm. Sensors 2019, 19, 5236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cheng, L.; Yu, T. A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems. Int. J. Energy Res. 2019, 43, 1928–1973. [Google Scholar] [CrossRef]
- Rathor, S.K.; Saxena, D. Energy management system for smart grid: An overview and key issues. Int. J. Energy Res. 2020, 44, 4067–4109. [Google Scholar] [CrossRef]
- Khan, A.R.; Mahmood, A.; Safdar, A.; Khan, Z.A.; Khan, N.A. Load forecasting, dynamic pricing and DSM in smart grid: A review. Renew. Sustain. Energy Rev. 2016, 54, 1311–1322. [Google Scholar] [CrossRef]
- Palensky, P.; Dietrich, D. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inform. 2011, 7, 381–388. [Google Scholar] [CrossRef] [Green Version]
- Fallah, S.N.; Deo, R.C.; Shojafar, M.; Conti, M.; Shamshirband, S. Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. Energies 2018, 11, 596. [Google Scholar] [CrossRef] [Green Version]
- Zhang, X.; Wang, J.; Zhang, K. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electr. Power Syst. Res. 2017, 146, 270–285. [Google Scholar] [CrossRef]
- Son, M.; Moon, J.; Jung, S.; Hwang, E. A short-term load forecasting scheme based on auto-encoder and random forest. In Proceedings of the 3rd International Conference on Applied Physics, System Science and Computers (APSAC), Dubrovnik, Croatia, 26–28 September 2018; pp. 138–144. [Google Scholar] [CrossRef]
- Abbasi, R.A.; Javaid, N.; Ghuman, M.N.J.; Khan, Z.A.; Rehman, S.U. Short Term Load Forecasting Using XGBoost. In Workshops of the International Conference on Advanced Information Networking and Applications; Springer: Berlin/Heidelberg, Germany, 2019; pp. 1120–1131. [Google Scholar] [CrossRef]
- Moon, J.; Jung, S.; Rew, J.; Rho, S.; Hwang, E. Combination of short-term load forecasting models based on a stacking ensemble approach. Energy Build. 2020, 216, 109921. [Google Scholar] [CrossRef]
- Sajjad, M.; Khan, S.U.; Khan, N.; Haq, I.U.; Ullah, A.; Lee, M.Y.; Baik, S.W. Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model. Sensors 2020, 20, 6419. [Google Scholar] [CrossRef] [PubMed]
- Lahouar, A.; Slama, J.B.H. Day-ahead load forecast using random forest and expert input selection. Energy Convers. Manag. 2015, 103, 1040–1051. [Google Scholar] [CrossRef]
- Moon, J.; Kim, K.-H.; Kim, Y.; Hwang, E. A Short-Term Electric Load Forecasting Scheme Using 2-Stage Predictive Analytics. In Proceedings of the IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 15–17 January 2018; pp. 219–226. [Google Scholar] [CrossRef]
- Park, S.; Moon, J.; Hwang, E. 2-Stage Electric Load Forecasting Scheme for Day-Ahead CCHP Scheduling. In Proceedings of the IEEE International Conference on Power Electronics and Drive System (PEDS), Toulouse, France, 9–12 July 2019. [Google Scholar] [CrossRef]
- Ryu, S.; Noh, J.; Kim, H. Deep Neural Network Based Demand Side Short Term Load Forecasting. Energies 2017, 10, 3. [Google Scholar] [CrossRef]
- Izonin, I.; Tkachenko, R.; Kryvinska, N.; Tkachenko, P.; Gregušml, M. Multiple Linear Regression Based on Coefficients Identification Using Non-Iterative SGTM Neural-Like Structure. In Proceedings of the International Work-Conference on Artificial Neural Networks, Gran Canaria, Spain, 12–14 June 2019; pp. 467–479. [Google Scholar] [CrossRef]
- Motepe, S.; Hasan, A.N.; Stopforth, R. Improving Load Forecasting Process for a Power Distribution Network Using Hybrid AI and Deep Learning Algorithms. IEEE Access 2019, 7, 82584–82598. [Google Scholar] [CrossRef]
- Kuan, L.; Yan, Z.; Xin, W.; Yan, C.; Xiangkun, P.; Wenxue, S.; Zhe, J.; Yong, Z.; Nan, X.; Xin, Z. Short-term electricity load forecasting method based on multilayered self-normalizing GRU network. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration, Beijing, China, 26–28 November 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Chitalia, G.; Pipattanasomporn, M.; Garg, V.; Rahman, S. Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks. Appl. Energy 2020, 278, 115410. [Google Scholar] [CrossRef]
- Sehovac, L.; Grolinger, K. Deep Learning for Load Forecasting Sequence to Sequence Recurrent Neural Networks with Attention. IEEE Access 2020, 8, 36411–36426. [Google Scholar] [CrossRef]
- Long-Term Energy Consumption & Outdoor Air Temperature for 11 Commercial Buildings—Datasets—OpenEI Datasets. Available online: https://openei.org/datasets/dataset/consumption-outdoor-air-temperature-11-commercial-buildings (accessed on 21 July 2020).
- Holidays and Observances around the World. Available online: https://www.timeanddate.com/holidays/ (accessed on 21 July 2020).
- Moon, J.; Park, S.; Rho, S.; Hwang, E. A comparative analysis of artificial neural network architectures for building energy consumption forecasting. Int. J. Distrib. Sens. Netw. 2019, 15. [Google Scholar] [CrossRef] [Green Version]
- Jung, S.; Moon, J.; Park, S.; Rho, S.; Baik, S.W.; Hwang, E. Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation. Sensors 2020, 20, 1772. [Google Scholar] [CrossRef] [Green Version]
- Dong-Nae Forecast (Digital Forecast). Available online: https://www.weather.go.kr/eng/weather/forecast/timeseries.jsp (accessed on 21 July 2020).
- Park, S.; Moon, J.; Jung, S.; Rho, S.; Baik, S.W.; Hwang, E. A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling. Energies 2020, 13, 443. [Google Scholar] [CrossRef] [Green Version]
- Moon, J.; Jung, S.; Park, S.; Hwang, E. Conditional Tabular GAN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting. IEEE Access 2020, 8, 205327–205339. [Google Scholar] [CrossRef]
- Poniszewska-Maranda, A.; Kaczmarek, D.; Kryvinska, N.; Xhafa, F. Studying usability of AI in the IoT systems/paradigm through embedding NN techniques into mobile smart service system. Computing 2018, 101, 1661–1685. [Google Scholar] [CrossRef]
- Teslyuk, V.; Kazarian, A.; Kryvinska, N.; Tsmots, I. Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems. Sensors 2021, 21, 47. [Google Scholar] [CrossRef]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Guo, T.; Xu, Z.; Yao, X.; Chen, H.; Aberer, K.; Funaya, K. Robust online time series prediction with recurrent neural networks. In Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, Canada, 17–19 October 2016; pp. 816–825. [Google Scholar] [CrossRef] [Green Version]
- Cho, K.; van Merriënboer, B.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014; Association for Computational Linguistics: Doha, Qatar, 2014; pp. 1724–1734. [Google Scholar]
- Klambauer, G.; Unterthiner, T.; Mayr, A.; Hochreiter, S. Self-Normalizing Neural Networks. In Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA, 4–9 December 2017. [Google Scholar]
- Jung, S.M.; Park, S.; Jung, S.W.; Hwang, E. Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities. Sustainability 2020, 12, 6364. [Google Scholar] [CrossRef]
- Balasundaram, S.; Meena, Y. Robust Support Vector Regression in Primal with Asymmetric Huber Loss. Neural Process. Lett. 2019, 49, 1399–1431. [Google Scholar] [CrossRef]
- Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention Is All You Need. In Advances in Neural Information Processing Systems 30; Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2017; pp. 5998–6008. [Google Scholar]
- Kwon, B.C.; Choi, M.J.; Kim, J.T.; Choi, E.; Kim, Y.B.; Kwon, S.; Sun, J.; Choo, J. Retainvis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans. Vis. Comput. Graph. 2018, 25, 299–309. [Google Scholar] [CrossRef] [Green Version]
- Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; et al. TensorFlow: A System for Large-Scale Machine Learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA, 2–4 November 2016; pp. 265–283. [Google Scholar]
- Moon, J.; Kim, J.; Kang, P.; Hwang, E. Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods. Energies 2020, 13, 886. [Google Scholar] [CrossRef] [Green Version]
- Rahman, R.; Otridge, J.; Pal, R. IntegratedMRF: Random forest-based framework for integrating prediction from different data types. Bioinformatics 2017, 33, 1407–1410. [Google Scholar] [CrossRef]
- Chen, T.; Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar] [CrossRef] [Green Version]
- Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825–2830. [Google Scholar]
Reference | Datasets | AI Methods | Granularity | Characteristics |
---|---|---|---|---|
Lahouar and Slama [12] | Tunisian Company of Electricity and Gas | RF with TSCV | Hourly | Model interpretation via variable importance |
Moon et al. [13] | Private university in Seoul, Korea | RF with TSCV | Daily | Reflecting recent power consumption trends |
Park et al. [14] | Commercial and industrial buildings in Korea | Stage 1: RF, XGB Stage 2: MLR with sliding window | Hourly | Performance reinforcement |
Ryu et al. [15] | Demand side load data provided by KEPCO | DNN and DBN | Hourly | Multistep-ahead load forecasting |
Izonin et al. [16] | Combined cycle power plant dataset | SGTM | Hourly | Less time consuming |
Motepe et al. [17] | South African power utility system | OP-ELM and LSTM | 30-min | Performance reinforcement |
Kuan et al. [18] | Electricity load from Junan, Shanhong province | GRU | 15-min | Multistep-ahead load forecasting |
Chitalia et al. [19] | Commercial buildings of five different types, in Bangkok, Hyderabad, Virginia, New York, and Massachusetts. | LSTM/BiLSTM, LSTM/BiLSTM with attention, CNN+LSTM, CNN+BiLSTM, and convLSTM/convBiLSTM | 15-min | Robust regardless of building types or locations |
Sehovac and Grolinger [20] | Commercial building | S2S RNN with attention | 5-min | Performance reinforcement |
Statistical | Building 1 | Building 2 | Building 3 | ||||||
---|---|---|---|---|---|---|---|---|---|
Weekdays | Holidays | Total | Weekdays | Holidays | Total | Weekdays | Holidays | Total | |
Minimum | 8.30 | 7.60 | 7.60 | 10.10 | 9.40 | 9.40 | 80.45 | 80.47 | 80.45 |
1st quartile | 17.40 | 12.90 | 14.70 | 20.80 | 17.00 | 19.10 | 124.11 | 109.53 | 117.22 |
Median | 33.90 | 17.70 | 23.50 | 41.20 | 20.90 | 32.20 | 199.72 | 131.14 | 157.82 |
Mean | 34.43 | 20.06 | 29.98 | 40.10 | 25.37 | 35.53 | 231.46 | 138.17 | 201.24 |
3rd quartile | 47.50 | 23.30 | 42.50 | 53.40 | 31.12 | 48.80 | 293.38 | 157.44 | 255.41 |
Maximum | 141.10 | 77.30 | 141.10 | 135.00 | 92.30 | 135.00 | 702.53 | 336.77 | 702.53 |
Standard deviation | 17.99 | 9.45 | 17.18 | 18.96 | 12.19 | 18.45 | 128.12 | 35.99 | 115.86 |
Count | 18,120 | 8136 | 26,256 | 18,120 | 8136 | 26,256 | 17,784 | 8520 | 26,304 |
Location | Richland, Washington | Richland, Washington | Seoul, South Korea | ||||||
Public access | Yes | Yes | No |
No. | Input Variable | Variable Type |
---|---|---|
1 | Monthx | Continuous [−1:1] |
2 | Monthy | Continuous [−1:1] |
3 | Dayx | Continuous [−1:1] |
4 | Dayy | Continuous [−1:1] |
5 | Hourx | Continuous [−1:1] |
6 | Houry | Continuous [−1:1] |
7 | Day of the weekx | Continuous [−1:1] |
8 | Day of the weeky | Continuous [−1:1] |
9 | Holiday | Binary [1: holiday, 0: weekday] |
10 | Temperature | Continuous |
11 | Historical loadD−7 | Continuous |
12 | Historical loadD−6 | Continuous |
13 | Historical loadD−5 | Continuous |
14 | Historical loadD−4 | Continuous |
15 | Historical loadD−3 | Continuous |
16 | Historical loadD−2 | Continuous |
17 | Historical loadD−1 | Continuous |
18 | Average load | Continuous |
Input Variable | Building 1 | Building 2 | Building 3 | |||
---|---|---|---|---|---|---|
PCC | p-Value | PCC | p-Value | PCC | p-Value | |
Monthx | 0.042 | *** | 0.118 | *** | −0.156 | *** |
Monthy | 0.117 | *** | 0.218 | *** | −0.177 | *** |
Dayx | 0.015 | ** | 0.018 | * | −0.009 | *** |
Dayy | 0.002 | *** | 0.008 | *** | 0.001 | *** |
Hourx | 0.134 | *** | 0.139 | *** | −0.153 | *** |
Houry | −0.541 | *** | −0.471 | *** | −0.640 | *** |
Day of the weekx | 0.333 | 0.322 | 0.110 | *** | ||
Day of the weeky | −0.048 | *** | −0.053 | *** | −0.255 | *** |
Holiday | −0.387 | *** | −0.369 | *** | −0.377 | *** |
Temperature | 0.014 | *** | −0.129 | *** | 0.319 | *** |
Historical loadD−7 | 0.841 | * | 0.819 | *** | 0.901 | ** |
Historical loadD−6 | 0.657 | *** | 0.642 | *** | 0.726 | *** |
Historical loadD−5 | 0.441 | *** | 0.432 | *** | 0.554 | ** |
Historical loadD−4 | 0.409 | *** | 0.403 | *** | 0.560 | *** |
Historical loadD−3 | 0.424 | *** | 0.421 | *** | 0.564 | *** |
Historical loadD−2 | 0.483 | *** | 0.481 | *** | 0.573 | *** |
Historical loadD−1 | 0.741 | *** | 0.740 | *** | 0.763 | *** |
Average load | 0.903 | *** | 0.885 | *** | 0.959 | *** |
Dataset Period | Buildings 1 and 2 | Building 3 |
---|---|---|
Entire collection period | 2009/01/09–2011/12/31 | 2015/01/08–2017/12/31 |
Training set | 2009/01/09–2010/12/31 | 2015/01/08–2016/12/31 |
Testing set | 2011/01/01–2011/12/31 | 2017/01/01–2017/12/31 |
Models | Package or Module | Selected Hyperparameters |
---|---|---|
MRF | MultivariateRandomForest | No. of trees: 128 [39] No. of features: 144 [39] |
DNN | TensorFlow | No. of hidden nodes: 289 [23] No. of hidden layers: 5 [23] Activation function: SELU [23] Optimizer: Adam Learning rate: 0.001 No. of epochs: 150 Batch size: 24 |
Park’s [14] Stage 1: RF, XGB Stage 2: MLR | scikit-learn xgboost | RF No. of estimators: 256 |
XGB No. of estimators: 256 Max depth: 4 | ||
MLR Sliding window size: 24 | ||
COSMOS [10] Stage 1: DNNs Stage 2: PCR | scikit-learn | DNN No. of hidden nodes: 13 No. of hidden layers: 2, 3, 4, 5 Activation function: ReLU Optimizer: Adam Learning rate: 0.001 No. of epochs: 150 Batch size: 24 |
PCR Principal components: 1 Sliding window size: 168 | ||
Kuan’s [18] | TensorFlow | GRU No. of cells: 100 [18] No. of hidden layers: 5 [18] Activation function: SELU [18] Time step: 12 [18] Batch size: 15 [18] |
LSTM ATT-LSTM GRU, ATT-GRU (Ours) | TensorFlow | LSTM, GRU No. of hidden nodes: 13 No. of hidden layers: 2 Activation function: SELU Optimizer: Adam Learning rate: 0.001 No. of epochs: 150 Batch size: 24 |
Month | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Jan. | 21.79 (27.40) | 20.36 (27.48) | 22.27 (30.62) | 13.89 (18.83) | 17.34 (26.21) | 14.10 (18.31) | 13.44 (18.06) | 13.86 (18.18) | 13.16 (17.77) |
Feb. | 21.32 (29.31) | 21.60 (28.55) | 18.49 (27.67) | 16.46 (20.55) | 17.38 (27.93) | 16.68 (20.82) | 15.67 (20.44) | 16.45 (20.55) | 14.51 (19.63) |
Mar. | 22.25 (32.89) | 22.16 (33.61) | 23.24 (32.71) | 14.85 (23.00) | 18.12 (32.12) | 15.24 (22.67) | 14.91 (20.61) | 14.88 (22.57) | 14.66 (21.33) |
Apr. | 14.24 (19.97) | 13.26 (21.43) | 20.82 (37.57) | 8.71 (14.62) | 11.11 (18.92) | 10.72 (16.90) | 10.77 (15.81) | 10.77 (15.99) | 9.12 (13.37) |
May | 7.97 (13.88) | 14.67 (22.25) | 16.06 (41.02) | 6.44 (11.71) | 12.13 (20.70) | 6.82 (12.97) | 7.12 (12.13) | 6.47 (13.51) | 6.39 (12.08) |
Jun. | 12.54 (13.33) | 18.19 (24.70) | 20.99 (39.29) | 12.56 (12.08) | 15.72 (24.11) | 12.66 (14.94) | 14.14 (18.67) | 12.69 (15.48) | 12.18 (14.11) |
Jul. | 14.22 (16.42) | 16.70 (27.32) | 19.21 (35.39) | 8.38 (11.37) | 16.10 (28.63) | 9.04 (16.11) | 7.76 (12.51) | 8.44 (15.07) | 7.67 (12.02) |
Aug. | 14.22 (15.51) | 19.40 (28.45) | 13.39 (22.53) | 7.52 (9.47) | 17.30 (28.49) | 9.73 (11.11) | 8.24 (10.91) | 9.44 (11.74) | 7.01 (8.74) |
Sep. | 14.20 (15.40) | 18.85 (26.70) | 28.65 (54.11) | 9.66 (13.12) | 17.18 (28.55) | 10.32 (12.44) | 11.41 (13.69) | 9.78 (12.54) | 8.96 (11.04) |
Oct. | 18.84 (22.80) | 15.14 (23.40) | 17.94 (28.88) | 12.13 (16.54) | 17.20 (23.46) | 12.40 (14.14) | 13.67 (17.76) | 12.12 (13.82) | 11.26 (13.51) |
Nov. | 31.33 (45.73) | 27.22 (42.51) | 44.11 (61.05) | 24.49 (40.11) | 32.57 (39.30) | 24.89 (35.82) | 20.88 (37.53) | 24.47 (35.22) | 20.76 (26.78) |
Dec. | 45.11 (47.95) | 41.43 (43.57) | 41.43 (45.24) | 36.64 (38.13) | 43.11 (42.18) | 37.28 (39.63) | 31.76 (41.74) | 36.71 (37.88) | 29.12 (31.31) |
Month | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Jan. | 15.81 (18.50) | 14.56 (20.43) | 13.95 (23.85) | 9.10 (11.80) | 13.35 (19.99) | 8.81 (12.64) | 7.64 (10.44) | 9.16 (12.12) | 7.16 (9.18) |
Feb. | 14.68 (18.83) | 17.12 (21.76) | 13.05 (20.24) | 10.37 (13.99) | 14.25 (18.86) | 12.73 (15.03) | 10.66 (13.61) | 10.37 (14.06) | 9.58 (12.27) |
Mar. | 12.58 (13.81) | 14.60 (17.78) | 13.32 (15.77) | 9.02 (11.30) | 14.56 (18.57) | 10.86 (9.19) | 9.81 (12.46) | 9.16 (11.98) | 9.09 (11.85) |
Apr. | 15.82 (16.17) | 16.86 (21.08) | 20.23 (27.87) | 10.60 (13.39) | 17.90 (21.28) | 12.93 (13.98) | 10.32 (13.38) | 10.70 (12.40) | 10.88 (12.56) |
May | 17.60 (16.80) | 17.62 (25.27) | 24.98 (34.07) | 7.92 (13.20) | 20.04 (24.90) | 8.70 (12.22) | 7.88 (11.99) | 9.08 (12.29) | 7.58 (11.44) |
Jun. | 25.31 (18.22) | 22.32 (26.79) | 33.24 (37.19) | 10.31 (11.19) | 22.16 (30.44) | 10.17 (13.99) | 10.12 (13.45) | 10.39 (13.91) | 9.56 (12.60) |
Jul. | 29.20 (21.19) | 27.35 (30.80) | 39.94 (46.91) | 7.63 (9.57) | 28.40 (37.39) | 8.82 (8.28) | 7.37 (9.67) | 8.69 (10.24) | 6.14 (8.15) |
Aug. | 28.13 (19.27) | 29.73 (30.13) | 30.88 (30.37) | 7.74 (8.63) | 24.06 (34.45) | 10.42 (11.48) | 7.61 (10.10) | 11.31 (13.25) | 6.88 (9.55) |
Sep. | 34.78 (23.66) | 27.71 (30.75) | 43.38 (55.64) | 9.56 (11.22) | 24.40 (34.78) | 9.90 (10.52) | 7.96 (10.43) | 8.67 (10.63) | 6.97 (9.56) |
Oct. | 34.34 (28.69) | 19.98 (27.05) | 31.51 (34.68) | 10.18 (13.62) | 21.81 (30.59) | 10.23 (9.79) | 8.48 (8.20) | 9.44 (10.27) | 8.67 (8.37) |
Nov. | 36.29 (37.48) | 33.78 (37.00) | 65.14 (62.40) | 21.93 (31.58) | 31.25 (48.13) | 19.78 (31.98) | 19.41 (28.97) | 20.75 (30.00) | 18.98 (26.88) |
Dec. | 45.07 (39.50) | 42.15 (39.32) | 38.69 (39.17) | 32.50 (34.73) | 32.29 (38.27) | 32.17 (37.40) | 28.80 (36.18) | 33.89 (38.71) | 26.77 (33.10) |
Month | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Jan. | 9.02 (12.02) | 14.95 (19.29) | 9.62 (11.71) | 9.38 (11.49) | 10.75 (12.37) | 9.19 (12.12) | 8.71 (11.16) | 9.33 (12.41) | 8.05 (10.81) |
Feb. | 7.60 (8.94) | 13.83 (16.95) | 14.74 (33.66) | 7.65 (9.18) | 9.28 (15.61) | 10.02 (13.94) | 6.94 (8.32) | 9.03 (11.69) | 6.04 (7.66) |
Mar. | 5.87 (8.89) | 11.28 (15.17) | 6.54 (8.93) | 4.53 (6.58) | 7.52 (10.48) | 5.76 (8.44) | 4.86 (7.16) | 7.61 (9.70) | 4.95 (7.70) |
Apr. | 4.28 (5.83) | 8.93 (12.83) | 5.51 (7.62) | 2.99 (4.03) | 7.00 (8.93) | 4.07 (6.55) | 3.17 (5.49) | 7.59 (9.29) | 2.38 (3.50) |
May | 6.06 (12.31) | 11.49 (16.55) | 8.26 (14.06) | 6.30 (11.95) | 7.84 (11.36) | 6.85 (8.91) | 5.23 (8.71) | 7.26 (9.72) | 5.11 (8.45) |
Jun. | 7.17 (13.10) | 14.76 (23.38) | 6.75 (13.84) | 5.74 (10.89) | 10.05 (17.63) | 5.23 (7.19) | 5.09 (8.84) | 9.77 (11.90) | 4.48 (6.92) |
Jul. | 10.18 (15.36) | 15.46 (26.38) | 9.33 (14.34) | 5.89 (8.63) | 12.26 (22.42) | 5.49 (9.54) | 5.63 (8.92) | 6.21 (8.14) | 5.33 (8.06) |
Aug. | 11.54 (17.38) | 16.03 (26.11) | 9.51 (15.32) | 6.00 (8.95) | 10.09 (19.16) | 7.14 (9.16) | 6.22 (10.21) | 7.62 (8.87) | 5.57 (8.48) |
Sep. | 6.26 (12.97) | 13.33 (19.38) | 9.24 (17.82) | 5.85 (12.00) | 9.26 (15.24) | 4.99 (6.19) | 4.38 (5.95) | 5.10 (6.00) | 3.57 (5.12) |
Oct. | 8.08 (13.18) | 13.17 (18.16) | 7.64 (11.87) | 7.97 (14.05) | 8.04 (10.92) | 7.43 (11.92) | 7.51 (10.17) | 7.53 (10.55) | 7.20 (9.72) |
Nov. | 7.50 (11.15) | 13.55 (18.28) | 8.76 (13.24) | 8.87 (12.67) | 9.32 (13.10) | 9.10 (14.75) | 8.52 (12.72) | 9.50 (13.99) | 8.10 (11.20) |
Dec. | 10.37 (16.75) | 16.39 (23.36) | 11.81 (17.74) | 9.81 (17.04) | 12.88 (19.98) | 11.20 (17.59) | 9.96 (16.77) | 11.02 (17.36) | 9.79 (15.96) |
Type | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Mon. | 18.75 (33.39) | 21.78 (33.81) | 21.76 (31.80) | 13.77 (25.03) | 15.47 (29.76) | 14.24 (27.35) | 13.47 (24.88) | 14.15 (26.12) | 12.86 (23.87) |
Tue. | 17.57 (25.52) | 18.86 (33.85) | 32.52 (58.81) | 13.08 (21.53) | 14.22 (29.76) | 13.47 (21.61) | 12.31 (20.50) | 13.41 (21.41) | 12.06 (20.09) |
Wed. | 18.06 (27.77) | 21.84 (33.14) | 18.63 (33.08) | 12.79 (19.81) | 17.79 (36.35) | 12.56 (19.70) | 12.23 (19.12) | 12.47 (20.23) | 11.23 (18.40) |
Thu. | 18.77 (28.41) | 22.08 (30.76) | 16.27 (26.51) | 12.96 (19.89) | 19.17 (38.47) | 12.29 (19.36) | 10.52 (16.45) | 11.75 (18.31) | 9.93 (14.95) |
Fri. | 21.20 (29.09) | 17.18 (27.93) | 17.18 (25.76) | 12.66 (23.02) | 17.05 (34.21) | 12.97 (19.08) | 12.66 (18.79) | 12.61 (19.51) | 11.43 (18.76) |
Sat. | 25.21 (39.61) | 21.22 (31.65) | 33.97 (45.92) | 19.31 (33.02) | 13.90 (28.41) | 15.03 (28.16) | 13.02 (19.00) | 14.43 (26.45) | 12.41 (19.82) |
Sun. | 19.30 (29.84) | 21.62 (32.29) | 26.92 (40.74) | 15.57 (29.39) | 14.88 (27.88) | 16.49 (29.22) | 15.48 (28.33) | 16.07 (29.54) | 14.66 (27.42) |
WD | 18.18 (28.46) | 20.16 (31.98) | 20.22 (37.28) | 12.46 (21.32) | 15.61 (31.17) | 13.43 (19.10) | 12.37 (19.27) | 13.06 (19.16) | 11.86 (18.56) |
HD | 23.58 (36.96) | 21.41 (32.04) | 32.20 (45.39) | 18.45 (33.35) | 14.39 (28.14) | 16.01 (28.57) | 14.26 (24.30) | 15.20 (27.96) | 13.93 (23.69) |
Type | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Mon. | 23.20 (24.79) | 24.56 (30.73) | 26.13 (28.65) | 12.56 (19.32) | 17.96 (27.26) | 13.59 (20.18) | 12.64 (19.15) | 13.48 (20.29) | 12.44 (18.28) |
Tue. | 22.42 (20.78) | 20.60 (25.60) | 33.89 (48.70) | 11.57 (17.54) | 12.57 (21.73) | 12.09 (18.19) | 11.53 (17.59) | 12.41 (18.86) | 11.12 (16.70) |
Wed. | 21.02 (20.81) | 24.28 (30.05) | 24.85 (32.74) | 9.66 (14.90) | 12.05 (20.06) | 10.24 (15.24) | 9.24 (14.52) | 10.25 (15.28) | 9.01 (13.56) |
Thu. | 21.71 (21.49) | 26.38 (31.33) | 24.63 (25.98) | 11.04 (16.95) | 16.75 (25.80) | 11.07 (16.50) | 10.06 (15.89) | 11.33 (16.42) | 9.67 (13.32) |
Fri. | 25.72 (24.44) | 20.61 (25.22) | 26.50 (28.33) | 11.40 (21.24) | 18.92 (29.06) | 12.84 (20.86) | 10.96 (16.15) | 11.85 (19.37) | 10.11 (16.01) |
Sat. | 36.37 (44.15) | 23.71 (28.44) | 43.78 (52.58) | 16.51 (28.31) | 21.34 (35.08) | 17.17 (26.07) | 15.60 (25.91) | 17.09 (26.47) | 14.58 (25.27) |
Sun. | 30.82 (34.81) | 24.76 (31.16) | 36.05 (42.14) | 13.45 (26.88) | 22.66 (33.83) | 13.91 (23.52) | 12.91 (21.09) | 13.55 (23.84) | 12.69 (20.56) |
WD | 22.12 (21.96) | 22.42 (28.97) | 25.62 (33.14) | 10.53 (17.13) | 18.85 (26.94) | 12.01 (19.08) | 10.45 (17.24) | 11.80 (19.14) | 10.15 (16.51) |
HD | 34.39 (40.14) | 24.55 (30.86) | 42.61 (51.59) | 16.31 (30.69) | 21.80 (33.77) | 16.84 (32.83) | 15.98 (30.24) | 16.55 (31.92) | 13.98 (27.80) |
Type | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Mon. | 7.89 (13.79) | 14.35 (23.50) | 8.24 (13.39) | 6.95 (11.24) | 9.65 (14.17) | 6.88 (11.61) | 6.47 (11.31) | 6.74 (11.64) | 6.20 (10.90) |
Tue. | 7.60 (8.94) | 13.13 (21.35) | 9.79 (16.18) | 7.08 (11.41) | 8.05 (13.17) | 7.69 (12.51) | 7.42 (12.11) | 7.61 (12.40) | 6.79 (11.74) |
Wed. | 5.87 (8.89) | 13.08 (20.18) | 8.85 (17.34) | 6.64 (10.23) | 6.32 (10.52) | 6.93 (10.25) | 6.61 (9.83) | 6.86 (10.13) | 6.05 (8.69) |
Thu. | 4.28 (5.83) | 13.87 (23.24) | 7.19 (12.32) | 5.25 (8.91) | 7.47 (13.16) | 6.16 (9.97) | 4.70 (8.10) | 5.82 (9.13) | 4.40 (8.16) |
Fri. | 6.06 (12.31) | 14.17 (23.64) | 10.43 (21.75) | 7.78 (12.44) | 8.12 (15.97) | 7.80 (12.84) | 7.01 (12.33) | 7.65 (12.67) | 6.54 (11.87) |
Sat. | 7.17 (13.10) | 12.74 (18.25) | 9.27 (13.94) | 6.81 (10.23) | 8.15 (16.48) | 6.45 (10.90) | 6.10 (9.39) | 6.37 (10.21) | 5.93 (8.87) |
Sun. | 10.18 (15.36) | 13.72 (22.04) | 8.81 (12.14) | 6.34 (8.88) | 8.08 (16.30) | 6.23 (8.58) | 5.99 (8.07) | 6.08 (8.93) | 5.17 (7.71) |
WD | 6.89 (12.16) | 13.27 (22.67) | 8.48 (16.24) | 6.02 (10.30) | 8.52 (16.99) | 6.60 (10.99) | 5.43 (10.25) | 6.33 (10.67) | 5.52 (9.84) |
HD | 9.79 (19.05) | 13.40 (21.09) | 9.88 (14.72) | 8.07 (12.68) | 8.51 (16.46) | 8.47 (12.89) | 7.78 (12.01) | 8.29 (12.70) | 7.95 (11.01) |
Training Time | MRF | DNN | Park’s [14] | COSMOS [10] | Kuan’s [18] | LSTM | ATT-LSTM | GRU | ATT-GRU |
---|---|---|---|---|---|---|---|---|---|
Building 1 | 361 | 503 | 274 | 1345 | 10,745 | 10,952 | 11,581 | 6974 | 7108 |
Building 2 | 372 | 498 | 281 | 1426 | 10,769 | 11,761 | 12,169 | 7362 | 7658 |
Building 3 | 364 | 500 | 282 | 1388 | 10,693 | 10,097 | 10,992 | 6885 | 7023 |
Average | 367 | 500 | 279 | 1386 | 10,736 | 10,937 | 11,581 | 7074 | 7263 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jung, S.; Moon, J.; Park, S.; Hwang, E. An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting. Sensors 2021, 21, 1639. https://doi.org/10.3390/s21051639
Jung S, Moon J, Park S, Hwang E. An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting. Sensors. 2021; 21(5):1639. https://doi.org/10.3390/s21051639
Chicago/Turabian StyleJung, Seungmin, Jihoon Moon, Sungwoo Park, and Eenjun Hwang. 2021. "An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting" Sensors 21, no. 5: 1639. https://doi.org/10.3390/s21051639
APA StyleJung, S., Moon, J., Park, S., & Hwang, E. (2021). An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting. Sensors, 21(5), 1639. https://doi.org/10.3390/s21051639