If we’re going to rely on more renewable energy sources, utilities will need better ways of predicting how much energy is needed, both in real time and over the long term. There has been much discussion and debate in the scientific community regarding the efficacy and suitability of machine learning techniques to help improve our understanding of local and global environment needs. This interesting information came to us from Earth.org in their article, “Can Machine Learning Help Tackle Climate Change?

Machine learning allows for predictive and probability-based calculations to be undertaken, which are useful tools for evaluating the benefits and costs of our actions in the present. It is useful for those active in climate science to understand the strengths and limitations of current machine learning techniques.

Algorithms already exist that can forecast energy demand, but they could be improved by taking into account finer local weather and climate patterns or household behavior. Efforts to make the algorithms more explainable could also help utility operators interpret their outputs and use them in scheduling when to bring renewable sources online.

Organizations need a better understanding of how artificial intelligence (AI) systems make decisions, so they will be better prepared to interpret the results. Explainable AI is used to describe an AI model, its expected impact, and its potential biases.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

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