Neural networks for state-of-the-art modeling tools.
In May 2017 we rolled out a groundbreaking project. For the first time in the gas industry and in Europe we wanted to leverage big data and advanced analytics to model and forecast gas delivery. 5 months later, a neural network based predictive tool was operational.
An integrated team has joined together combining diverse expertise in order to incorporate the largest choices of alternate parameterizations and achieve a more predictive model. In fact, the neural network system adopted is an ensemble of 6 pairs of dynamic models: a multi-model forecasting method which significantly reduces errors in model output.
Balancing the system is crucial for the market: not just physically but also from a commercial point of view. Network operators’ improvements in information flow towards market players and regulatory bodies support liquidity and transparency. We applied state-of-the-art machine learning techniques to deliver the most reliable and transparent information to all users.
-26% Average error
-31% Maximum error
-32% Errors > 10%
Evolution of Snam’s Forecasting Models
Linear AutoRegressive Model
(Computer Aided Sales Series Analysis Nullifying Daily Residuals Autocorrelation)
(Dynamic Adjusted Forecasting Neural Ensemble)
Machine Learning algorithms
The model is trained to forecast target variables and evaluate the impact of each explanatory variable
It performs a “feature selection” on the explanatory variables reducing their number
The objective function is determined by an algorithm only on the basis of the data structure
Relations between variables are determined by non deterministic models
Multidimensional segmentation of the country according to temperature, altitude, humidity, precipitations, etc.
Daily time series of provisional and definitive data
Including the ability to capture critical non standard dates such as bank and floating holidays and potential long weekends
Hourly time series of SCADA data points
Electricity sale and purchase transactions: data related to hourly energy blocks that are traded for the next day.
Entry points of nomination, which indicates the gas delivery of a specified volume during the gas day at a network exit/entry point.
Multi-parametric weather forecasts with multiple updates per day.
Schedule management of heating systems in accordance with the distinction in climatic zones currently in force in Italy.