- Dai, Y., & Zhao, P. (2020). A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization. Applied Energy, 279, 115332.
| Novelty | real-time price/holiday and non-holiday |
|---|---|
| previous-limitation | the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid |
| data/code | simulation of Singapore/https://data.mendeley.com/datasets/hvz7g6r3mw/2 |
- Dong, Y., Zhang, H., Wang, C., & Zhou, X. (2021). A novel hybrid model based on Bernstein polynomial with mixture of Gaussians for wind power forecasting. Applied Energy, 286, 116545.
| Novelty | The original wind power series is first decomposed into several intrinsic mode functions by complete ensemble empirical mode decomposition, and then a Bernstein polynomial forecasting model with mixture of Gaussians is constructed. Finally, a population- based multi-objective state transition algorithm with parallel search mechanism is developed to optimize the parameters of the hybrid model. |
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| previous-limitation | owing to the intermittence and nonlinearity of wind power time series/improve the accuracy and stability of wind power forecasting |
| data/code | a wind farm in Xinjiang |
- Buzna, L., De Falco, P., Ferruzzi, G., Khormali, S., Proto, D., Refa, N., ... & van der Poel, G. (2020). An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations. Applied Energy, 116337.
| Novelty | The hierarchical approach is applied to decompose the problem into sub-problems at low-level regions, which are resolved through standard probabilistic models |
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| previous-limitation | Accurate electric vehicle load forecasting becomes essential to reduce adverse effects of electric vehicle integration into the grid. |
| data/code | EVnetNL dataset |
- Mashlakov, A., Kuronen, T., Lensu, L., Kaarna, A., & Honkapuro, S. (2021). Assessing the performance of deep learning models for multivariate probabilistic energy forecasting. Applied Energy, 285, 116405.
awesome
| Novelty | This study bridges the gap between the adoption of novel global deep-learning-based models for probabilistic multivariate forecasting in the deep learning community and the applicability of these methods for energy forecasting. |
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| previous-limitation | capturing the complex dependences and uncertainties of power system operation/the adoption of global deep learning models for multivariate energy forecasting in power systems is far behind the developments in the deep learning research field |
| data/code | https://github.com/aleksei-mashlakov/multivariate-deep-learning |
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Kong, W., Jia, Y., Dong, Z. Y., Meng, K., & Chai, S. (2020). Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting. Applied Energy, 280, 115875.
Novelty deep whole-sky-image learning architectures for very short- term solar photovoltaic generation forecasting, of which the lookahead windows concern the scales from 4 to 20 min previous-limitation At the operating stage, the forecasting accuracy of renewables has a direct influence on energy scheduling and dispatching. data/code self-collected