An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption
Abstract
:1. Introduction
2. Data and Methods
2.1. Data Source
2.2. Logarithmic Mean Divisia Index (LMDI) Method
2.2.1. The Index Decomposition Method
2.2.2. The Improved MGM (1, n) Model
2.2.3. Assessment of the Improved MGM (1, n) Model Forecasting Precision
2.2.4. Particle Swarm Optimization
2.2.5. Validation of the Improved MGM (1, n) Model
3. Empirical Results and Discussion
3.1. The Driving Factors of Electricity Demand in Linfen City
3.1.1. The Driving Factors of Industrial Electricity Consumption
3.1.2. The Driving Factor of Residential Electricity Consumption
3.2. Forecasting Results
3.2.1. The Forecasting Results of GDP
3.2.2. Prediction of Electricity Consumption in Linfen City
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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k | MGM (1, 2) | IMGM (1, 2) | Improved MGM (1, 2) | |||
---|---|---|---|---|---|---|
(1–7) | A | B | A | B | A | B |
MAPE (%) | 0.284 | 0.163 | 0.044 | 0.078 | 0.020 | 0.018 |
k | Actual Value | MGM (1, 2) | IMGM (1, 2) | Improved MGM (1, 2) | ||||
---|---|---|---|---|---|---|---|---|
8 | 31.67 | 33.97 | 31.54 | 33.86 | 31.66 | 33.99 | 31.67 | 33.95 |
9 | 37.55 | 38.29 | 37.36 | 38.17 | 37.52 | 38.38 | 37.53 | 38.27 |
k | MGM (1, 2) | IMGM (1, 2) | Improved MGM (1, 2) | |||
---|---|---|---|---|---|---|
8 | 0.411 | 0.324 | 0.032 | 0.059 | 0.012 | 0.060 |
9 | 0.506 | 0.313 | 0.080 | 0.235 | 0.049 | 0.046 |
MAPE (%) | 0.459 | 0.319 | 0.056 | 0.147 | 0.031 | 0.053 |
RMSE | 0.2055 | 0.1151 | 0.736 | 0.0652 | 0.0652 | 0.02 |
Year | ||||
---|---|---|---|---|
2011 | 53,199.74 | −132,894 | 43,317.66 | 142,775.7 |
2012 | 258,306.5 | −27,275.4 | 33,601.71 | 251,980.1 |
2013 | 402,640.2 | 66,553.8 | −17,913.8 | 354,000.2 |
2014 | 515,845.8 | 98,037.6 | −65,516.7 | 483,324.8 |
2015 | 369,863.1 | 97,469.4 | −12,5161 | 397,554.5 |
2016 | 423,062.3 | 118,228.1 | −15,6176 | 461,010.7 |
2017 | 256,254.5 | −35,982.1 | −15,6070 | 448,307.2 |
2018 | 318,246.8 | 60,838.6 | −19,4715 | 452,123.7 |
2019 | 506,871.5 | 112,777.7 | −21,6961 | 611,054.5 |
2020 | 845,671.5 | 192,952.4 | −23,6792 | 889,511.4 |
2021 | 1,083,334 | −246,456 | −65,114.9 | 1,394,905 |
2022 | 968,506.8 | −586,773 | 64,350.83 | 1,490,928 |
Year | ||||
---|---|---|---|---|
2011 | −9969.59 | −9969.59 | 33,115.4 | −1295.82 |
2012 | 445.0893 | 445.0893 | 39,827.92 | −2775.01 |
2013 | 4926.278 | 4926.278 | 43,482.36 | −4286.64 |
2014 | 17,195.26 | 17,195.26 | 43,876.43 | −5689.68 |
2015 | 23,690.13 | 23,690.13 | 38,292.49 | −6877.62 |
2016 | 34,352.55 | 34,352.55 | 44,045.56 | −8306.12 |
2017 | 24,946.75 | 24,946.75 | 72,485.09 | −9847.84 |
2018 | 32,565.05 | 32,565.05 | 91,675.15 | −12,336.2 |
2019 | 30,257.12 | 30,257.12 | 106,881 | −14,154.1 |
2020 | 38,849.57 | 38,849.57 | 120,250.5 | −16,116 |
2021 | 15,108.46 | 15,108.46 | 179,440.9 | −20,241.3 |
2022 | 12,891.09 | 12,891.09 | 228,620.9 | −22,203.9 |
Year | Primary Industry | Secondary Industry | Tertiary Industries | GDP |
---|---|---|---|---|
2023 | 0.148056 | 0.932828 | 0.916491 | 1.997375 |
2024 | 0.158663 | 0.999766 | 0.982936 | 2.141365 |
2025 | 0.170074 | 1.071757 | 1.054226 | 2.296056 |
2026 | 0.182338 | 1.149116 | 1.130707 | 2.462161 |
2027 | 0.195512 | 1.232199 | 1.212752 | 2.640464 |
2028 | 0.209658 | 1.321395 | 1.300762 | 2.831814 |
2029 | 0.224841 | 1.417126 | 1.395167 | 3.037135 |
2030 | 0.241135 | 1.519854 | 1.496431 | 3.25742 |
Year | (100 Billion kWh) | (100 Billion kWh) | (100 Billion kWh) |
---|---|---|---|
2023 | 2.450 | 0.368 | 2.818 |
2024 | 2.678 | 0.408 | 3.086 |
2025 | 2.939 | 0.452 | 3.391 |
2026 | 3.236 | 0.5020 | 3.738 |
2027 | 3.572 | 0.558 | 4.13 |
2028 | 3.953 | 0.621 | 4.574 |
2029 | 4.382 | 0.692 | 5.074 |
2030 | 4.866 | 0.771 | 5.637 |
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Li, Z.; Lu, J. An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption. Energies 2024, 17, 3872. https://doi.org/10.3390/en17163872
Li Z, Lu J. An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption. Energies. 2024; 17(16):3872. https://doi.org/10.3390/en17163872
Chicago/Turabian StyleLi, Zhenhua, and Jinghua Lu. 2024. "An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption" Energies 17, no. 16: 3872. https://doi.org/10.3390/en17163872