Energy Forecasting with LSTM Neural Network

Several methods have been used in energy forecasting over the years. Methods from different disciplines, such as ARMA and ARIMA models from econometrics and probabilistic and regression models from the domain of statistics, which also has an intersection with the symbolic AI field, to name a few. This experiment forecasts energy demand using the Long Short-Term Memory (LSTM) Neural Network models.

Data Source

National Grid Electricity System Operators (National Grid ESO)

Google Collab Code

References

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451–2471.

Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7), 1235–1270. https://doi.org/10.1162/neco_a_01199