Colloquia

Summary

The central premise of this study is to develop a decision-making tool to make informed decisions regarding technology choices and energy systems. When clean energy technologies need to be implemented in an industrial facility, there are a plethora of options possible to fulfill a requirement. In response to climate change and increasing energy prices, it has become a strategic and moral necessity for industry to move towards clean energy systems. Therefore, the main question of this research is ‘How can a decision-making methodology be developed to determine the most suitable energy system for a steam-driven process industry?’. The result of this study has yielded a comprehensive methodology that addresses technologies from a holistic perspective, encompassing technical applicability, maintenance, project implementation, financial performance, and sustainability considerations. Furthermore, studies and experiments have been conducted on models for predicting electricity prices. The electricity prices are crucial to simulate the financial performance of the composed energy system scenarios. Forecasting models based on Auto-Regressive Integrated Moving Average (ARIMA), Prophet, and Long Short-Term Memory (LSTM) have been found as potential candidates. Consequently, it has been observed that the LSTM model has superior results in predicting future electricity prices compared to the others. Additionally, a method has been method has been introduced to deal with future price uncertainty. Conclusively, In the domain of decision-making processes, the combination of the Analytical Hierarchy Process and Weighted Sum Model method, incorporating criteria like Lifetime, Area, and Scalability, along with an original pointing system, has proven to be a constructive and consistent approach to decision-making among various energy systems.