Benchmarks and Custom Package for Electrical Load Forecasting

by   Zhixian Wang, et al.

Load forecasting is of great significance in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between load forecasting and traditional time series forecasting. On the one hand, load forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. On the other hand, the load is largely influenced by many external factors, such as temperature or calendar variables. In addition, the scale of predictions (such as building-level loads and aggregated-level loads) can also significantly impact the predicted results. In this paper, we provide a comprehensive load forecasting archive, which includes load domain-specific feature engineering to help forecasting models better model load data. In addition, different from the traditional loss function which only aims for accuracy, we also provide a method to customize the loss function based on the forecasting error, integrating it into our forecasting framework. Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.


Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach

Load forecasting is very essential in the analysis and grid planning of ...

A New State-of-the-Art Transformers-Based Load Forecaster on the Smart Grid Domain

Meter-level load forecasting is crucial for efficient energy management ...

Uncovering Dominant Features in Short-term Power Load Forecasting Based on Multi-source Feature

Due to the limitation of data availability, traditional power load forec...

Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources

Recent developments related to the energy transition pose particular cha...

Hierarchical Demand Forecasting Benchmark for the Distribution Grid

We present a comparative study of different probabilistic forecasting te...

Dynamic mode decomposition for forecasting and analysis of power grid load data

Time series forecasting remains a central challenge problem in almost al...

The Age of Correlated Features in Supervised Learning based Forecasting

In this paper, we analyze the impact of information freshness on supervi...

Please sign up or login with your details

Forgot password? Click here to reset