At my role in DataFy Tech, I had the opportunity to leverage LSTM networks for a project based on predicting future energy consumption for a group of industrial units. Given the temporal nature of the data, we decided that time series forecasting using LSTM Networks would be the ideal approach.
The process involved preprocessing the data into a suitable format for LSTM, which was then divided into training and testing sets. We designed and trained the LSTM network using PyTorch, monitoring its performance to mitigate overfitting. The sequence length and the number of layers for the LSTM model were decided based on a series of experiments to get the best results.
This resulted in a highly accurate predictive model that offered significant value to our client. By effectively forecasting energy demand, we managed to achieve a reduction of 7% in unnecessary energy costs, validating the potential of LSTM networks in predictive analytics.