# Short-term Electric Load Forecasting Using TensorFlow and Deep Auto-Encoders

## TL;DR

This paper proposes a novel short-term electric load forecasting model using Deep Auto-Encoder Networks and TensorFlow, improving accuracy, stability, and scalability over traditional neural network methods in big data scenarios.

## Contribution

It introduces a new distributed forecasting approach combining DAENs with TensorFlow, addressing over-fitting and convergence issues of traditional neural networks.

## Key findings

- Enhanced prediction accuracy compared to traditional methods
- Improved model stability and scalability
- Effective handling of big data for load forecasting

## Abstract

This paper conducts research on the short-term electric load forecast method under the background of big data. It builds a new electric load forecast model based on Deep Auto-Encoder Networks (DAENs), which takes into account multidimensional load-related data sets including historical load value, temperature, day type, etc. A new distributed short-term load forecast method based on TensorFlow and DAENs is therefore proposed, with an algorithm flowchart designed. This method overcomes the shortcomings of traditional neural network methods, such as over-fitting, slow convergence and local optimum, etc. Case study results show that the proposed method has obvious advantages in prediction accuracy, stability, and expansibility compared with those based on traditional neural networks. Thus, this model can better meet the demands of short-term electric load forecasting under big data scenario.