Improving Gas Load Forecasts with Big Data

This is my first gas load forecasting paper. We introduce the methodology, models and lessons learned from the 2015 RWE npower gas load forecasting competition, where the BigDEAL team ranked Top 3. The core idea is to leverage comprehensive weather information to improve gas load forecasting accuracy.

Citation
Jingrui Xie and Tao Hong, "Improving gas load forecasts with big data". Natural Gas & Electricity, vol. 32, no. 10, pp 25–30, 2016. doi:10.1002/gas.21905 (working paper available HERE)

Improving Gas Load Forecasts with Big Data

Jingrui Xie and Tao Hong

Abstract

The recent advancement in computing, networking, and sensor technologies has brought a massive amount of data to the business world. Many industries are taking advantage of the big data along with the modern information technologies to make informed decisions, such as managing smart cities, predicting crime activities, optimizing medicine based on genetic defects, detecting financial frauds, and personalizing marketing campaigns. According to Google Trends, the public interest in big data now is 10 times higher than it was five years ago (Exhibit 1). In this article, we will discuss gas load forecasting in the big data world. The 2015 RWE npower gas load forecasting challenge will be used as the case study to introduce how to leverage comprehensive weather information for daily gas load forecasting. We will also extend the discussion by articulating several other big data approaches to forecast accuracy improvement. Finally, we will discuss a crowdsourcing, competition-based approach to generating new ideas and methodologies for gas load forecasting.