Optimal Reservoir Operations using Long Short-Term Memory Network

TL;DR

This paper introduces an LSTM-based approach for real-time reservoir inflow forecasting, demonstrating improved accuracy over traditional methods and providing a baseline for flood and drought prediction using 20 years of data from Bhakra Dam.

Contribution

The work develops a novel LSTM-based inflow forecasting model with an anomaly detection baseline, applicable to any basin with minimal modifications.

Findings

LSTM outperforms traditional forecasting methods.

The approach effectively handles non-stationarity and non-linearity.

Demonstrated on 20 years of data from Bhakra Dam.

Abstract

A reliable forecast of inflows to the reservoir is a key factor in the optimal operation of reservoirs. Real-time operation of the reservoir based on forecasts of inflows can lead to substantial economic gains. However, the forecast of inflow is an intricate task as it has to incorporate the impacts of climate and hydrological changes. Therefore, the major objective of the present work is to develop a novel approach based on long short-term memory (LSTM) for the forecast of inflows. Real-time inflow forecast, in other words, daily inflow at the reservoir helps in efficient operation of water resources. Also, daily variations in the release can be monitored efficiently and the reliability of operation is improved. This work proposes a naive anomaly detection algorithm baseline based on LSTM. In other words, a strong baseline to forecast flood and drought for any deep learning-based prediction model. The practicality of the approach has been demonstrated using the observed daily data of the past 20 years from Bhakra Dam in India. The results of the simulations conducted herein clearly indicate the supremacy of the LSTM approach over the traditional methods of forecasting. Although, experiments are run on data from Bhakra Dam Reservoir in India, LSTM model, and anomaly detection algorithm are general purpose and can be applied to any basin with minimal changes. A distinct practical advantage of the LSTM method presented herein is that it can adequately simulate non-stationarity and non-linearity in the historical data.