Artificial intelligence is revolutionizing the energy sector: all the companies that are able to use their potential correctly and in a timely manner will benefit from it.
According to the report “Artificial Intelligence and the Circular Economy. AI as a Tool to Accelerate the Transition” written by the Ellen MacArthur Foundation, intelligent technologies could solve complex problems faster than humans and help organizations accelerate the resource flows of the circular economy.
At Drive2Data we study, research and develop innovative solutions that can help make the world more sustainable and secure. Preserving the future of our planet is a commitment that we can no longer extend.
Given the topicality of the issue, we decided to deepen some aspects related to the energy transaction:
Regulating the production of energy
The readiness of electricity is influenced by many factors: supply of natural resources, availability of renewable sources, international energy market and more
The task of the National Authorities is to monitor all these parameters to better manage the production and import of energy from abroad, so as to ensure the needed supply at all times.
The use of AI tools allows a more accurate and instantaneous analysis of the situation, also allowing the management of local storage systems and a better integration of renewable sources.
The number of storage systems has grown significantly in recent years as solar and wind power plants proliferate. Their role is to store electricity and make it available when it is most needed, balancing supply and demand and helping to stabilize the network.
Artificial Intelligence tools are able to optimize the use of energy systems: they can process a large number of data relating to external environmental conditions and the state of the plant. Intelligent algorithms allow to analyse such information accurately and with the following objectives:
- make predictions of system performance in order to predict errors or malfunctions;
- develop analysis on the performance and reliability of the system itself;
- manage the consumption of energy generated.
In the future, parking spaces with charging stations for electric cars can also be included in machine learning and deep learning algorithms with the aim of planning and redistributing charges according to the needs of saving and reducing load peaks.
Analysis of consumption data with neural networks
Artificial Intelligence applications can be used by companies and institutions to predict future energy demand by analysing past consumption.
The main aim is to integrate predictive models into operational processes to develop sustainable strategies.
Consumption forecasts are based on the analysis of historical data in order to identify recurring behaviour. A Data Cleansing strategy is essential to implement Artificial Intelligence solutions. Raw data sets can often prove unusable due to errors and missing information. A complete and well-structured database is the prerequisite for effective and ecological actions.
Artificial Neural Networks (ANN) techniques are supporting research on predicting time series data. Historical information combined with other atmospheric variables and the state of the network allow you to plan corrective strategies to reduce energy costs.
According to the International Energy Agency (IEA), AI will be instrumental in transforming global energy systems radically over the coming years, making them more interconnected, reliable and sustainable.
Intelligent communities and automated energy management models
The Renewable Energy Community (ERC) is an autonomous legal entity based on the open and voluntary participation of companies, individuals, entities or municipalities, which can help society in its path of energy transition.
The objective of CERs is to provide community-wide environmental, economic or social benefits to their shareholders or members
AI technology and tools can be applied to energy communities for multiple services, including:
- real-time monitoring of energy performance with consumption control;
- Data Analytics with particular attention to environmental and economic values;
- reducing unnecessary energy waste.
These are Energy Data Intelligence systems that become effective Business Intelligence tools that can support the management and administration of the communities in question.