When it comes to energy, artificial intelligence (AI) contributes to the real-time monitoring of power grids, more accurate predictions of power fluctuations, and the development of new strategies to work with geothermal energy sources. Smart Energy International brought this interesting information to our attention in their article, “How to advance data analysis and AI in the grid – dena recommendations.”
The combination of data analysis and AI delivers added value to grid systems and their operators and is key for transitioning to and managing renewables. The direct effect of energy on everything – health, education, communications, defense, etc. – is a clear sign of its importance to humanity.
But while energy is an essential element, negative consequences of non-renewable energy consumption – such as environmental deterioration, resource depletion, and energy dependence – put energy efficiency alternatives and/or solutions in the spotlight. New technologies are gaining prominence in the face of the alarming need to both reduce greenhouse gas emissions and develop a more sustainable economic model.
Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
Sponsored by Access Innovations, changing search to found.