期刊
SENSORS
卷 22, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/s22124363
关键词
energy management; adaptive models; generation modalities; load forecasting; machine learning; model deterioration; power stability; Smart Grid
资金
- Universiti Teknologi PETRONAS (UTP)
- Yayasan Universiti Teknologi Petronas (YUTP) through the Grant Cost Center [YUTP 015LC0-360]
This study proposes a novel adaptive framework to overcome existing limitations in electrical load forecasting models. The framework improves model accuracy and adapts to dynamic parameter variations, making it applicable in smart grid and different generation mode environments.
Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.
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