Climate change impacts the power system globally. It also creates a challenge for Indonesia's energy transition, which aims for net-zero emissions by 2060. Aside from decarbonization efforts, planning for this transition adds a challenge due to the deeply uncertain nature of climate change. This refers to a condition where planners cannot agree on models, probabilities, or even which variables to prioritize. That degree of climate uncertainty has not yet been addressed in Indonesia's current power systems planning approach. Failure to address these uncertainties could bring significant vulnerabilities to Indonesia's future power system. Furthermore, only a small number of studies on power systems planning in Indonesia have addressed these climate uncertainties, and even then, only in a limited way. This paper offers a conceptual recommendation of an adaptive planning approach as one potential method to address these uncertainties. The approach is based on Dynamic Adaptive Pathways Planning (DAPP), which comes from the decision-making under deep uncertainty (DMDU) taxonomy. It supports planners in exploring a range of possible futures, considering policies and uncertainties, and enabling more robust decision-making.

The aftermath of the COVID-19 pandemic in 2019 resulted in a decrease in Java Bali's total load instead of the usual increasing trend. The loads also exhibit different characteristics in their daily, weekly, and monthly load profiles in each region. The basic statistical coefficient method used to forecast the load introduces a higher possibility of error and inaccuracies in operational planning. A different approach is necessary to achieve higher accuracy in load forecasting. One method to predict reliable trends is deep learning, a subfield of machine learning, which can synthesize the learning curve based on available data. A method called Long-Short Term Memory (LSTM), included in Deep Learning and popularized by researchers since 2000, has shown better accuracy in forecasting. This paper focuses on reviewing the LSTM method for short-term load forecasting in the Java Bali power system using several additional inputs. The method demonstrates an accurate learning curve after the addition of several input parameters.