Enhanced Electricity Demand Forecasting through Transparent Model Parameterization and Hyperparameter Optimization

The power sector faces significant challenges due to the increase in electricity gadgets and usage. Gadgets and electricity appliances like air conditioning play an essential role all over the home and office sectors. For a few decades, the usage of electricity has increased. External factors like climatic conditions are also affecting the usage of electricity. Based on the economic growth of India, Tamil Nadu also increased in multi-growth industries. As of now, the usage of electric vehicles (two-wheelers as well as four-wheelers) is also rapidly increasing, which also leads to some fluctuation in electricity demand. These transformations change the patterns of electricity consumption and require careful management to ensure a stable supply. The suggested system uses XGBoost, a sophisticated gradient boosting technique suitable for tabular data and capable of handling nonlinearity. NeuralProphet handles time series-specific patterns (seasonality, trend) in a hybrid model, while XGBoost captures other patterns using designed features. This study introduces a new method for forecasting time series using NeuralProphet and XGBoost. NeuralProphet uses an additive model to model trends, seasonality, and event-based data. To improve Neural Prophet’s forecasts, residual errors generate more features, which are then fed into an XGBoost model for improved outcomes. XGBoost modeling then improves Neural Prophet’s ability to detect more nonlinear interactions that were not previously addressed. Finally, the predicting outputs from both models are combined, taking advantage of both NeuralProphet and FBProphet which is effective at catching reoccurrence and long-term oscillatory behaviors, and XGBoost, which explores the residual data structure.

کلیدواژه ها:

NeuralProphet ، XGBoost ، Timeseries Modeling ، Nonlinearity ، Seasonality