Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
Abstract
:1. Introduction
- Analyze and compare the relevant papers that have been proposed and discussed LSTM models on solar irradiance prediction.
- Identify better models among standalone and hybrid models of LSTM to predict solar irradiation and PV power by comparing the features of prediction parameters.
- Discuss in depth regarding the characteristics and mechanism of LSTM and how it is able to integrate with other methods to improve the performance of solar prediction accuracy.
2. Related Works
3. LSTM
4. Hybrid Models
5. Evaluation Metrics
6. Analysis of Past Studies
6.1. Accuracy
6.2. Types of Input Data
6.3. Forecast Horizon
- Very short-term forecast (ahead by 1 min to several minutes);
- Short-term forecast (ahead by 1 h or several hours to 1 day or 1 week);
- Medium-term forecast (ahead by 1 month to 1 year); and
- Long-term forecast (ahead by 1–10 years).
6.4. Type of Season and Weather
6.5. Training Time
7. Future Directions
- In terms of comparing and analyzing the available source code, not all the reviewed papers provided the data source codes; it is recommended for future works to find the data sources to describe the data and analyze their differences.
- Regarding performance evaluation, it is difficult to compare accuracy efficiently between the prediction models due to several main factors such as different evaluation metrics used, weather conditions of selected regions, forecasting horizons, size of input parameters, and so on. Thus, it is suggested to find specific research papers that discuss or review similar factors as mentioned, to compare the performance effectively.
- This paper has mostly reviewed very short-term and short-term forecast horizons for solar irradiance and solar power forecasting (Table 3 and Table 4). For future work, it is recommended to expand the review on medium-term and long-term forecast horizons by applying various combinations of DL and ML models to enhance the existing hybrid models.
8. Conclusions
- In terms of predicting solar irradiance, hybrid models outperform standalone models. In particular, the evaluation measures of hybrid models are significantly lower than those of standalone models. Among the hybrid models, CNN–LSTM requires complex input data, such as images, because it includes a CNN layer.
- When evaluating model performance, training time must be considered. Because hybrid models must extract two types of feature (i.e., spatial and temporal features), they take a longer time to process data compared to standalone models.
- The prediction accuracy for models that run a large batch size of data is lower when compared to other prediction models that use small data batch sizes. This is because more data are required to be extracted, and there is a more complicated process to produce the most accurate prediction.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ref. | Criteria | |||
---|---|---|---|---|
LSTM | Hybrid Model | Evaluation Metrics | Analysis of Past Studies | |
[8] | X | − | √ | X |
[9] | − | √ | √ | X |
[10] | √ | − | √ | √ |
[11] | − | − | √ | √ |
[12] | X | − | √ | √ |
[13] | − | √ | √ | √ |
[14] | − | − | − | √ |
Evaluation Metric | Equation |
---|---|
Error | |
MAE | |
MAPE | |
MBE | |
rMBE | |
rRMSE | |
RMSE |
Ref. | Forecast Horizon | Time Interval | Model | Input Parameter | Historical Data Description | Error Metrics |
---|---|---|---|---|---|---|
[1] | Ahead by 1 h | Hourly | LSTM–CNN |
| 1 January 2015 to 31 December 2019 (5 years) | Average RMSE: Los Angeles: 57.983 W/m2 San Diego: 47.826 W/m2 San Francisco: 66.023 W/m2 |
[29] |
| Hourly | MSCA–CLSTM | GHI | 2018 (1 year) | Average RMSE: Columbus: 0.0177 W/m2 San Antonio: 0.0183 W/m2 Detroit: 0.0183 W/m2 |
[30] |
| 15 min | CNN–LSTM |
| 1 January 2016 to 1 January 2017 | RMSE (6 steps): 5.79–34.89 W/m2 |
[31] | Multiple forecast horizon (1 day to 8 months) | 30 min | CLSTM | GSR | 1 January 2006 to 31 August 2018 | RMSE (W/m2): 1 day: 8.189 1 week: 16.011 2 weeks: 14.295 1 month: 32.872 |
[26] | Ahead by 1 h | Hourly | CNN–LSTM |
| 1 January 2006 to 31 December 2012 | Average MAE: Dallas: 41.88 W/m2 San Jacinto: 52.00 W/m2 Zapata: 43.66 W/m2 Moore: 37.26 W/m2 Lamb: 37.20 W/m2 |
[32] | Ahead by 1 h | Hourly | CEEMDAN-CNN–LSTM | Solar irradiance | 6 year data | Average RMSE: 38.49 W/m2 |
[33] | Ahead hourly every day | Hourly | LSTM |
|
| RMSE: 76.245 W/m2 |
[34] | Ahead by 1 h | Hourly | LSTM |
| 2000 to 2014 | Average 24-h RMSE: 80.0 W/m2 |
Ref. | Forecast Horizon | Interval Data | Model | Input Variables | Historical Data Description | Size PV Power (kW) | Error Metrics |
---|---|---|---|---|---|---|---|
[35] |
| 7.5 min | CNN-ALSTM |
| October 2014 to September 2018 | N/A | Overall RMSE: 7.5 min: 1.30 15 min: 1.40 30 min: 2.04 |
[36] |
| 10 min | 5D CNN–LSTM |
| 1 year data (2019–2020) | 1.70 | RMSE: 10 min: 0.0830 30 min: 0.2257 60 min: 0.4593 90 min: 0.7289 120 min: 1.0588 150 min: 1.4438 180 min: 2.0570 |
[37] | 1 day | 15 min | BCLSTM + IFFS | Numerical weather prediction (NWP) data | 1 January 2017 to 31 December 2018 (2 years) | N/A | RMSE: 0.1075 kW |
[38] |
| 7.5 min | ALSTM |
| October 2014 to September 2018 | 20.0 | Overall RMSE: 7.5 min: 1.39 15 min: 1.60 30 min: 1.81 60 min: 2.09 |
[10] | Ahead by 1 h | 5 min | WPD–LSTM |
| 1 June 2014 to 12 June 2016 | 26.5 | Average RMSE: 0.2357 |
[39] | N/A | 5 min | LSTM–CNN |
| Half-year data (53,280 samples) | N/A | RMSE: 0.621 |
[40] | Ahead by 1 h | Hourly | PCA–LSTM | The dataset has 49 features
| The first 24 historical data points | N/A | NRMSE: 0.0472% |
[41] |
| 15 min | Auto-LSTM |
| 2014–2015 (2 years) | 1.30 | Daily forecasting RMSE: Smart meter 1: 4.4414 Smart meter 2: 7.1925 |
[42] | Ahead hourly | 15 min | LSTM | PV power | 13 January 2010 to 29 January 2010 | 20,000 | RMSE (ahead by 1 h): 0.841 |
[43] | Ahead by 1 h | Hourly | DRNN–LSTM |
| 1 January 2018 to 1 February 2018 | 106.60 | RMSE: 7.536 |
[44] | Ahead by 1 day | Every 1 min or 5 min | LSTM | PV power | One month | N/A | Average RMSE: 0.512 |
[45] | Ahead by 1.5 h | 15 min | Stacked LSTM | PV power | 1 September 2016 to 31 January 2019 (84,768 observation) | N/A | RMSE: 0.09394 |
[46] | Ahead by 1 h | Hourly | LSTM-FC |
| 1 January 2018 to 31 December 2018 | N/A | RMSE: 2.5605 |
[47] | Ahead by 1 day | 5 min | EMD-SCA-LSTM |
| 1-year data (2017) | 5.83 | RMSE: 0.5283 kW MAE: 0.3063 kW R2: 0.9210 |
Step Ahead | MAE (W/m2) | RMSE (W/m2) | MAPE (%) | R2 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
LSTM | CNN | CNN–LSTM | LSTM | CNN | CNN–LSTM | LSTM | CNN | CNN–LSTM | LSTM | CNN | CNN–LSTM | |
1 | 6.61 | 6.51 | 3.83 | 10.43 | 9.82 | 5.79 | 10.19 | 11.29 | 7.50 | 0.998 | 0.998 | 0.999 |
2 | 12.15 | 11.69 | 7.32 | 20.46 | 18.09 | 11.71 | 51.74 | 37.45 | 19.64 | 0.993 | 0.994 | 0.997 |
3 | 18.39 | 16.73 | 10.61 | 31.12 | 26.41 | 18.18 | 28.62 | 52.21 | 31.87 | 0.984 | 0.988 | 0.994 |
4 | 24.16 | 21.81 | 13.68 | 40.33 | 34.53 | 23.48 | 70.51 | 91.84 | 50.08 | 0.974 | 0.979 | 0.99 |
5 | 31.21 | 27.26 | 17.01 | 52.5 | 43.11 | 29.25 | 30.99 | 52.79 | 55.47 | 0.957 | 0.969 | 0.985 |
6 | 36.89 | 32.38 | 20.07 | 62.27 | 50.99 | 34.89 | 44.81 | 127.29 | 59.28 | 0.942 | 0.957 | 0.979 |
Season | Types of Weather | Error | WPD–LSTM | LSTM | GRU | RNN | MLP |
---|---|---|---|---|---|---|---|
Winter | Sunny (Day 1) | MBE (kW) | −0.0055 | −0.0058 | 0.1588 | −0.0085 | −0.0284 |
MAPE (%) | 1.7526 | 1.7744 | 2.1019 | 2.633 | 5.5833 | ||
RMSE (kW) | 0.2466 | 1.2541 | 1.2399 | 1.2468 | 1.1944 | ||
Cloudy (Day 2) | MBE (kW) | 0.1127 | −0.0497 | 0.0184 | −0.0901 | 0.2429 | |
MAPE (%) | 1.7365 | 0.1276 | 1.9913 | 2.7622 | 6.1295 | ||
RMSE (kW) | 0.1773 | 1.1279 | 0.2206 | 0.2868 | 0.6075 | ||
Rainy (Day 3) | MBE (kW) | −0.0214 | −0.1913 | 0.1651 | −0.3158 | −0.0495 | |
MAPE (%) | 6.7328 | 8.4150 | 10.8690 | 9.3110 | 10.7191 | ||
RMSE (kW) | 0.4374 | 2.2336 | 2.0876 | 2.1223 | 1.9916 | ||
Spring | Sunny (Day 4) | MBE (kW) | 0.1425 | −0.0239 | 0.0377 | −0.0240 | 0.3277 |
MAPE (%) | 2.0973 | 1.3243 | 2.0199 | 3.1087 | 6.8559 | ||
RMSE (kW) | 0.2250 | 0.1643 | 0.2456 | 0.3431 | 0.7173 | ||
Cloudy (Day 5) | MBE (kW) | −0.0675 | 0.1165 | 0.5523 | 0.0711 | −0.2168 | |
MAPE (%) | 8.1383 | 15.3881 | 14.9651 | 13.0762 | 15.4708 | ||
RMSE (kW) | 0.1453 | 0.2759 | 0.6452 | 0.4222 | 0.3312 | ||
Rainy (Day 6) | MBE (kW) | 0.0566 | 0.2304 | 0.5502 | 0.0674 | 0.1121 | |
MAPE (%) | 3.8080 | 9.9553 | 14.8235 | 11.3013 | 8.3763 | ||
RMSE (kW) | 0.2807 | 0.8107 | 1.0036 | 0.8604 | 0.7572 | ||
Summer | Sunny (Day 7) | MBE (kW) | 0.0481 | 0.1115 | 0.2936 | −0.1382 | 0.3617 |
MAPE (%) | 2.6031 | 8.4936 | 10.8292 | 8.8545 | 10.8997 | ||
RMSE (kW) | 0.2664 | 0.9701 | 1.0748 | 0.8514 | 1.0822 | ||
Cloudy (Day 8) | MBE (kW) | 0.0183 | 0.2012 | 0.5218 | −0.0583 | 0.1048 | |
MAPE (%) | 3.4360 | 13.0028 | 11.8370 | 14.8472 | 11.5344 | ||
RMSE (kW) | 0.2382 | 0.8398 | 0.9323 | 0.8812 | 0.7810 | ||
Rainy (Day 9) | MBE (kW) | −0.0127 | 0.0924 | 0.4668 | −0.2068 | −0.3127 | |
MAPE (%) | 3.8936 | 9.8571 | 12.5799 | 16.0052 | 14.7068 | ||
RMSE (kW) | 0.1253 | 0.3009 | 0.5805 | 0.4993 | 0.4479 | ||
Autumn | Sunny (Day 10) | MBE (kW) | −0.0799 | −0.2050 | 0.0959 | −0.1813 | 0.2752 |
MAPE (%) | 2.0367 | 7.4015 | 8.3304 | 7.8951 | 8.3189 | ||
RMSE (kW) | 0.1929 | 0.7395 | 0.8029 | 0.7778 | 0.7495 | ||
Cloudy (Day 11) | MBE (kW) | 0.0049 | 0.1174 | 0.5692 | −0.1111 | 0.1729 | |
MAPE (%) | 3.7923 | 5.0279 | 9.9234 | 7.0087 | 14.4850 | ||
RMSE (kW) | 0.2576 | 1.0540 | 1.2110 | 1.1365 | 1.0643 | ||
Rainy (Day 12) | MBE (kW) | 0.0029 | −0.0799 | 0.1202 | −0.3103 | 0.3244 | |
MAPE (%) | 4.3427 | 8.3508 | 9.3104 | 7.3294 | 14.8202 | ||
RMSE (kW) | 0.3903 | 2.4216 | 2.3687 | 2.4275 | 2.4343 |
Season | Indicator | Methods | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
C-C-L | C-L | C-S | C-B | C-A | LSTM | SVM | BP | ARIMA | Per. | ||
Spring | RMSE (W/m2) | 42.87 | 56.76 | 64.97 | 64.46 | 79.88 | 79.63 | 84.62 | 82.20 | 112.46 | 126.76 |
nRMSE (%) | 17.88 | 25.46 | 29.15 | 28.92 | 35.84 | 35.72 | 37.96 | 36.87 | 50.45 | 56.87 | |
MAE (W/m2) | 22.80 | 37.74 | 36.09 | 35.98 | 41.84 | 42.21 | 51.09 | 41.87 | 63.77 | 76.89 | |
Summer | RMSE (W/m2) | 47.60 | 55.15 | 58.73 | 65.07 | 82.44 | 70.62 | 73.09 | 70.94 | 110.09 | 125.63 |
nRMSE (%) | 17.34 | 21.68 | 23.09 | 25.58 | 32.41 | 27.76 | 28.72 | 27.89 | 43.28 | 49.39 | |
MAE (W/m2) | 26.80 | 36.76 | 37.60 | 39.44 | 43.09 | 39.12 | 41.14 | 35.66 | 65.76 | 80.06 | |
Autumn | RMSE (W/m2) | 37.59 | 47.19 | 47.76 | 50.86 | 69.79 | 54.86 | 59.71 | 55.49 | 103.07 | 122.39 |
nRMSE (%) | 17.65 | 19.59 | 19.83 | 21.11 | 28.98 | 22.78 | 24.79 | 23.04 | 42.79 | 50.81 | |
MAE (W/m2) | 19.66 | 29.59 | 27.79 | 29.01 | 36.14 | 27.28 | 38.96 | 28.38 | 60.32 | 75.99 | |
Winter | RMSE (W/m2) | 25.97 | 38.19 | 46.82 | 45.31 | 50.23 | 47.26 | 54.24 | 48.27 | 81.98 | 107.96 |
nRMSE (%) | 15.73 | 19.60 | 24.04 | 23.26 | 25.79 | 24.26 | 27.85 | 24.78 | 42.09 | 55.42 | |
MAE (W/m2) | 13.25 | 22.14 | 28.33 | 23.69 | 25.65 | 20.18 | 32.44 | 21.86 | 41.92 | 64.54 | |
Annual | RMSE (W/m2) | 38.49 | 49.87 | 55.10 | 57.07 | 71.72 | 64.37 | 68.93 | 65.57 | 102.61 | 121.75 |
nRMSE (%) | 17.23 | 21.85 | 24.14 | 25.00 | 31.42 | 28.20 | 30.20 | 28.73 | 44.96 | 53.34 | |
MAE (W/m2) | 20.50 | 31.56 | 32.45 | 32.03 | 36.68 | 32.20 | 40.90 | 31.95 | 57.94 | 74.64 |
LSTM | CNN | CNN–LSTM | LSTM–CNN | |
---|---|---|---|---|
Training time (s) | 70.490 | 787.494 | 983.701 | 871.606 |
Running time (s) | 5.439 | 5.425 | 8.692 | 7.196 |
Model | Time (s) |
---|---|
5D LSTM | 9.1394 |
2D CNN–LSTM | 8.0362 |
5D CNN–LSTM | 69.1148 |
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Jailani, N.L.M.; Dhanasegaran, J.K.; Alkawsi, G.; Alkahtani, A.A.; Phing, C.C.; Baashar, Y.; Capretz, L.F.; Al-Shetwi, A.Q.; Tiong, S.K. Investigating the Power of LSTM-Based Models in Solar Energy Forecasting. Processes 2023, 11, 1382. https://doi.org/10.3390/pr11051382
Jailani NLM, Dhanasegaran JK, Alkawsi G, Alkahtani AA, Phing CC, Baashar Y, Capretz LF, Al-Shetwi AQ, Tiong SK. Investigating the Power of LSTM-Based Models in Solar Energy Forecasting. Processes. 2023; 11(5):1382. https://doi.org/10.3390/pr11051382
Chicago/Turabian StyleJailani, Nur Liyana Mohd, Jeeva Kumaran Dhanasegaran, Gamal Alkawsi, Ammar Ahmed Alkahtani, Chen Chai Phing, Yahia Baashar, Luiz Fernando Capretz, Ali Q. Al-Shetwi, and Sieh Kiong Tiong. 2023. "Investigating the Power of LSTM-Based Models in Solar Energy Forecasting" Processes 11, no. 5: 1382. https://doi.org/10.3390/pr11051382