Every 5 minutes consumers and generators of electricity in Australia’s wholesale National Electricity Market must decide how much power to buy or sell at a given price. When there is an oversupply of electricity the market price becomes negative, costing generators millions of dollars per year. Until recently renewable generators assumed these negative price periods as a cost of doing business, but the increased frequency of negative price periods and the deployment of utility scale batteries has made the application of highly adaptive time series forecasting essential to integrating renewable energy and battery storage into the NEM. We will describe how Fluence uses Machine Learning to efficiently operate these renewable energy assets.