Environmental and economic constraints
For many industrial operators, on-site energy consumption like fuel, oil, electricity or lubricant is a major expense. Since the beginning of the 21st century, energy costs have only increased , and although it may swing over a period of time, it makes up a significant proportion of their operating costs.
On top of that, it has become a real ecological challenge to address, as more and more regulations and norms pile up and as the public awareness over pollution and climate change grows.
Energy efficiency, which is called by some the “fifth fuel” – after coal, gas, nuclear, and renewables(*) has made it critical for companies to gain visibility into their energy expenditure and be able to prioritize efficiently cost-reduction measures.
The energy sector, which is generally regarded as conservative, has undertaken significant technological and organizational changes in the last few years. They are now ready and already driving innovative changes through AI and data science initiatives.
*According to 2015 McKinsey Global Institute report on Technologies that could transform how industries use energy
Current limitations to smart energy management
Having considered the opportunities, potential obstacles are still numerous, but not limited to:
- Data is often fragmented and heterogeneous, coming from different systems that run on-premise and are not compatible with each other:
- Industrial tools (ERP, OEM, FSM…)
- IoT sensors
- Data requires extensive reprocessing work in order to be used
- Work environment are often outdated and in remote areas with low connectivity
Organizations going towards energy efficiency also need to take into account the scalability of the services they aim to create, as it involves equipping a large number of users with customized analytics tools.
One platform for all your energy analytics challenges
ForePaaS helps leading energy companies to rapidly integrate and consolidate data from different enterprise systems, external providers, and IoT networks to feed machine learning models specific to the energy market.
Run them in production at enterprise scale to:
- Enable decision-making support on energy purchases
- Identify potential savings
- Detect earlier consumption anomalies or frauds