Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN
Abstract
:1. Introduction
2. Big Data Analysis in Power Systems
3. AC Demand and Load Forecasting
4. LMA-based ANN Approach
5. Performance Assessment Indices
6. Case Studies
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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LF Approach | Prediction Horizon | Achievable Benefits | Influencing Factors |
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Very Short-Term |
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Short-Term |
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Medium-Term |
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Long-Term |
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Very Long-Term |
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Multiple Linear Regression | SCG-Based ANN | LMA-Based ANN | |||||
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Hour | Actual | Predicted | %AE | Predicted | %AE | Predicted | %AE |
1 | 129.60 | 144.31 | 5.26 | 131.49 | 1.46 | 136.41 | 5.26 |
2 | 124.27 | 141.02 | 5.65 | 131.29 | 5.65 | 131.29 | 5.65 |
3 | 120.89 | 139.27 | 6.12 | 131.78 | 9.01 | 128.29 | 6.12 |
4 | 119.48 | 139.07 | 7.13 | 133.15 | 11.44 | 128.00 | 7.13 |
5 | 119.96 | 141.09 | 8.98 | 135.88 | 13.27 | 130.73 | 8.98 |
6 | 123.18 | 147.60 | 11.51 | 141.03 | 14.49 | 137.35 | 11.51 |
7 | 129.91 | 159.20 | 13.14 | 148.64 | 14.42 | 146.98 | 13.14 |
8 | 138.11 | 166.99 | 10.66 | 154.49 | 11.86 | 152.84 | 10.66 |
9 | 147.38 | 170.49 | 5.74 | 158.07 | 7.25 | 155.85 | 5.74 |
10 | 155.07 | 172.81 | 1.25 | 160.52 | 3.52 | 157.01 | 1.25 |
11 | 159.14 | 173.72 | 1.54 | 161.90 | 1.73 | 156.69 | 1.54 |
12 | 160.29 | 172.65 | 2.66 | 162.31 | 1.26 | 156.02 | 2.66 |
13 | 159.40 | 170.55 | 2.62 | 162.10 | 1.69 | 155.23 | 2.62 |
14 | 157.09 | 168.46 | 1.39 | 161.77 | 2.98 | 154.90 | 1.39 |
15 | 154.99 | 165.95 | 0.28 | 161.18 | 3.99 | 155.43 | 0.28 |
16 | 155.96 | 165.71 | 1.54 | 161.07 | 3.27 | 158.36 | 1.54 |
17 | 162.86 | 172.54 | 1.09 | 163.30 | 0.27 | 164.64 | 1.09 |
18 | 167.66 | 180.35 | 0.07 | 165.67 | 1.19 | 167.78 | 0.07 |
19 | 164.53 | 178.28 | 1.89 | 164.33 | 0.12 | 167.64 | 1.89 |
20 | 158.47 | 173.62 | 3.92 | 162.05 | 2.26 | 164.68 | 3.92 |
21 | 152.15 | 168.57 | 4.23 | 159.26 | 4.67 | 158.59 | 4.23 |
22 | 143.47 | 161.65 | 4.97 | 154.34 | 7.58 | 150.60 | 4.97 |
23 | 134.31 | 152.91 | 5.01 | 147.31 | 9.68 | 141.04 | 5.01 |
24 | 124.61 | 143.29 | 4.73 | 138.69 | 11.30 | 130.50 | 4.73 |
Maximum | 180.35 | 22.54 | 165.67 | 14.49 | 167.78 | 13.14 | |
MSE % | 9.68 | 8.30 | 5.57 | ||||
MAPE % | 4.9529 | 4.2782 | 2.9221 | ||||
MAE(Wh) | 7.2294 | 6.2456 | 4.2371 | ||||
Daily MAPE % | 4.3743 | 4.1249 | 2.5348 |
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Share and Cite
Waseem, M.; Lin, Z.; Yang, L. Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN. Big Data Cogn. Comput. 2019, 3, 36. https://doi.org/10.3390/bdcc3030036
Waseem M, Lin Z, Yang L. Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN. Big Data and Cognitive Computing. 2019; 3(3):36. https://doi.org/10.3390/bdcc3030036
Chicago/Turabian StyleWaseem, Muhammad, Zhenzhi Lin, and Li Yang. 2019. "Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN" Big Data and Cognitive Computing 3, no. 3: 36. https://doi.org/10.3390/bdcc3030036