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Performance evaluation of various hyperparameter tuning strategies for forecasting uncertain parameters used in solving stochastic optimization problems

This paper focuses on the performance assessment of various hyperparameter tuning techniques and algorithms used by Long Short-Term Memory (LSTM) networks in forecasting uncertain parameters. An energy intensive industry installed with a hybrid energy system consisting of solar photo voltaic (PV) panels, waste to energy (WTE) plants and main electricity grid is considered as a case study for demonstration. Among the three case studies considered for hyperparameter tuning, the first case study deals with a manual tuning approach. While the second one focuses on a tuning strategy using Optuna with grid search algorithm, the third case study concentrates on adopting Optuna with a Bayesian optimization framework. The overall objective entails generating an optimal forecast of the uncertain parameters (in this case solar irradiance and main grid electricity price) using LSTM adopting various hyperparameter tuning techniques in order to solve a day-ahead energy scheduling Mixed Integer Linear Programming (MILP) cost and carbon emission minimization problem.