Making neural networks available for energy consumption prediction
We created LSTEnergy, a Long-Term-Short-Memory energy forecasting model built using Blossom Sky and real-world smart meter data acquired in 2020. It is a time-series forecasting model that predicts future events using time-based previous data and is typically performed once every week per smart meter. It works by training it on a collection of past data and then attempting to predict future values using the 'smartmeter' dataset. The model is trained to learn a function that transfers a series of prior observations as input to an output observation, and is used to optimize particular processes with AI. Long Short-Term Memory (LSTM) is a form of recurrent neural network (RNN) that can learn long-term dependencies.