Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode...

Climate change alters river flow dynamics, creating uncertainty for hydropower operations. Prior research has shown that accurate inflow forecasts help manage reservoirs better. However, no prior work had resolved how preprocessing methods could improve forecasting accuracy while reducing computational costs. Existing models often rely on full datasets without filtering, which may include irrelevant variables. This gap motivated the development of new preprocessing techniques. Researchers have tested various machine learning models, but few have combined them with causal inference. The need for efficient and accurate forecasting remains unmet. Climate variability increases the importance of adaptive forecasting tools. This paper addresses that need by introducing a novel preprocessing framework.

The study aims to develop a preprocessing framework that improves short-term inflow forecasting accuracy. It focuses on hydropower systems in climate-affected regions. The problem is the high computational cost and low accuracy of traditional forecasting methods. The motivation is to enhance reservoir operations amid climate uncertainty. The proposed solution combines causal inference with multiresolution analysis. This approach allows for feature selection that is relevant to inflow prediction. The framework is designed to work with multiple machine learning models. It is tested in a real-world dam-regulated river in Norway.

The study uses Causal Variational Mode Decomposition (CVD) as a preprocessing step. CVD is based on multiresolution analysis and causal inference. It filters input features to retain only those relevant to inflow prediction. The framework is tested with four forecasting algorithms, including LSTM. Data comes from a river system downstream of a hydropower reservoir in Norway. The dataset includes historical inflow measurements and environmental variables. The model is validated using real-world data to assess forecasting accuracy. The study compares CVD-LSTM results with baseline and standard LSTM models.

CVD-LSTM achieved a 70% reduction in forecasting error compared to a baseline scenario. It also reduced error by 25% compared to a standard LSTM model. The preprocessing step improved accuracy without increasing computational load. The results suggest that feature selection enhances model performance. The study found that CVD effectively identifies relevant variables for inflow prediction. The framework is compatible with multiple forecasting algorithms. Validation on real-world data confirms the method's practical utility. These findings support the use of CVD in hydropower forecasting systems.

The study concludes that CVD improves forecasting accuracy by selecting relevant features. It reduces computational costs while maintaining performance. The framework is a complementary step to machine learning models. The results suggest that preprocessing enhances model reliability. The study supports the use of CVD in climate-affected hydropower systems. The findings align with the authors' claim that preprocessing is valuable for forecasting. The method is validated using real-world data from Norway. The authors propose that CVD can be applied to other forecasting problems.