The proliferation of sensors, smart meters, and other instrumentation in the water treatment sector has led to a near inundation of data. Machine learning can interpret and utilize these massive quantities of data to produce forecasts and process models to aid utilities in long-term and short-term decision-making.
Forecasts based on machine learning are not a novel concept. Neural networks, which are algorithms designed to mimic the way the human mind makes connections between information, were introduced into the power generation industry in the 1980s. In the decades since, these technologies have been honed and refined.
The power industry began leveraging machine learning and predictive modeling in order to generate forecasts about energy consumption. Historically, water utilities lacked the need for demand forecasts that plague energy generation. However, the combination of water scarcity and aging infrastructure is driving water utilities to follow suit in order predict consumption patterns and develop long-term investment plans.