The Challenge

Anticipate & manage the electricity prices volatility

With the increase of renewables in the energy mix, the supply-demand balance on the grid is becoming increasingly challenging to achieve. It results in higher volatility of electricity prices especially for short-term markets ( e.g. Day-ahead, Intraday, Balancing) .

Are you exposed to the volatility of electricity markets via your electricity contract or via direct market participation ? Are you considering to increase your exposure to Day-ahead, Intraday or Imbalance prices but are reluctant to take too much risk ? You want to valorise your flexibility but choosing the good arbitrage between markets or between time periods is not easy ?

The Solution

Machine learning algorithms to capture the best opportunities for your business!

The use of accurate electricity price forecasts based on machine learning technologies, such as deep neural networks, and high energy market expertise allows industrial and market participants to tap into new sources of revenues. The N-SIDE Electricity Price Forecasts enable to better anticipate the prices fluctuations, better manage the associated risks and convert this into potential opportunities. Our machine learning algorithms leverage 50K free parameters and use about 500 data sources among which the weather conditions, the load of the electrical grid and the prices observed in the recent past.

The N-SIDE Electricity Price Forecasts are available on a smart platform that gives you the possibility to adapt the tool in function of your own business and its specificities. You will also benefit from a better visualisation of your performance and from identifying new opportunities thanks to a custom alerting.

Within the platform you will determine your risk profile, define your price threshold and your reactivity level, while keeping control on your business and making your own decisions.

The use of the N-SIDE Electricity Price Forecasts will enable you to :

Take optimal decisions from week-ahead to real-time, in different electricity markets,

with the N-SIDE Electricity Price Forecasts !

Day-Ahead Market

Forecast of the Day-ahead electricity prices up to seven days in advance, with hourly granularity

Available for Belgium and UK. Ongoing extension to other countries

Intraday Market

Forecast of Intraday electricity prices to leverage your flexibility at the best price 

Coming soon

Imbalance Market

Forecast of the Imbalance price ahead of real-time or in real-time with up to minute per minute update

Available in Belgium and The Netherlands. 

Cutting-edge Machine learning algorithms to ensure the best performance!

  • Get access to a panel of forecasts built on well-suited  algorithms to deal with the non-linearity of the electricity market by leveraging high number of hidden layers in the neural network and by combining the best of Artificial intelligence and the market expertise.
  • Benefit from new releases to continuously leverage new market insights and innovative algorithms.


User-friendly and transparent platform to keep control on your business!

  • Define your price thresholds and reactivity levels that fit with your business operations.
  • Select different levels of risk exposure to tailor the forecasts recommendations to your risk management policy
  • Visualize the frequently updated forecasts, track their performance over time with associated savings





The Results

New sources of energy costs optimization


Detecting peak prices and plan your energy flexibility accordingly to reduce your average electricity price paid

Capturing in advance the top hours with the highest prices or lowest prices and increase the valorization of your on-site electricity production (e.g. CHP units)

Reducing Imbalance costs with our Imbalance risk / Intraday forecast by managing your risk of Imbalance at the best moment and leveraging your flexibility in real-time

Leverage price differences between markets and arbitrate optimally between them to minimize your risk exposure

of savings per flexible MW on yearly basis with the Spot Price Forecast

free parameters leveraged by machine learning algorithms

of savings per flexible MW on yearly basis with Imbalance Price Forecast

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