Forecasting Energy Demand Using a Long Short-Term Memory Network

The datasets I worked with were a combination of publically-available information on weather and load for regions covered by ISO New England, the corporation responsible for distributing energy across the 6 New England states. I used hourly data from October 2018 to present, which at the time of this project constituted 3 years of data. Since the regions controlled by ISO-NE were likely to have different energy demands due to each area's specific geographical attributes, I decided to simplify the problem by honing in on only one of the 8 regions. I selected the Connecticut ISO zone. The challenge at hand was to see if I could accurately forecast one-hour-ahead load for the Connecticut ISO zone given past values of the features I had available.