Perpustakaan Digital ITB

The world is facing the danger of a future energy crisis, compounded by the negative impacts of fossil fuel use as the current primary energy source. One way to address both issues is by transitioning from fossil fuels to renewable energy. Urban areas are suitable locations for the development of renewable energy due to their high energy demands. Urban regions have high energy consumption because of their large populations and their role as centers for economic and industrial activities. Net-zero carbon initiatives can be implemented in urban areas to reduce greenhouse gas emissions by replacing fossil fuels with renewable energy sources. Solar energy is a renewable energy option that can be easily applied in urban environments, as it can be installed on rooftops, eliminating the need for open land. However, a well-designed plan is necessary to implement net-zero carbon in urban areas. Since solar energy is intermittent, there are times when energy cannot be supplied by solar energy, so installing batteries is essential for storing solar energy during the daytime. A good plan requires calculating the necessary capacity of PV (Photovoltaic) and batteries for each building. Optimal capacity can be determined by calculating the balance between solar energy production and energy consumption for each building. In urban areas, energy production is complex due to the presence of tall buildings that can block sunlight to reach rooftops. Additionally, energy consumption varies from building to building. Therefore, careful calculations are needed to determine the appropriate solar cell and battery capacity for each building. To determine the optimal PV and battery capacity, this study follows several stages: reconstructing detailed 3D models of urban buildings, integrating physical and meteorological parameters to achieve high temporal and spatial resolution for solar energy observations, and then combining each building’s solar energy potential with its energy consumption to calculate the optimal solar cell and battery capacity. Three energy fulfillment scenarios—20%, 50%, and 100%—are used to assess the required capacity for gradual net-zero carbon implementation. In this study, remote sensing and geospatial data were integrated to achieve the objective. To determine building height, this research used DSM (Digital Surface Model) data: DEMNAS (Digital Elevation Model Nasional) Indonesia and DSM AW3D (Alos World 3D), that integrated with Sentinel-1. The source data were combined with building polygons to obtain detail building height. To assess PV potential, this research utilized geostationary satellite observation data (AMATERASS) with a 10-minute temporal resolution. The data were solar radiation, temperature, and wind velocity. Demand data were generated based on the general hourly statistic electricity household demand in Indonesia. In this study, 3D building models were created by integrating multi-machine learning methods and datasets. The best results for low, medium, and high building height classes were achieved using a combined dataset with the random forest method, AW3D with the decision tree method, and a combined dataset with the decision tree method, with Mean Absolute Error (MAE) values of 1.333 m, 2.487 m, and 13.898 m, respectively. This reconstruction resulted in an R² value of 0.754. Using the building height data, physical analysis was performed using shadow modeling and the sky view factor (SVF) for each pixel with a size of 1 x 1 m. Physical and meteorological models were integrated to determine detailed PV potential for each pixel, showing hourly energy potential in Jakarta and Bandung ranging from 260 to 420 Wh/Wp. Next, the PV energy potential was integrated with the electricity consumption of each building to calculate the optimal solar cell and battery capacity. The difference of scenario energy fulfillment can change the optimal PV and battery capacity use in every building. The larger energy fulfilment makes the larger optimal capacity. The study found that the median solar cell capacities for small, medium, large, very large, and extra-large buildings in Jakarta were 0.8, 4.2, 17, 42.1, and 117.4 kWh, respectively, while in Bandung, the capacities were 0.9, 2.2, 6.8, 16.8, and 37.4 kWh. The median batteries for small, medium, large, very large, and extra-large buildings in Jakarta were 1.9, 11.4, 47.3, 119.2, and 351.4 kWh, while in Bandung, they were 2.2, 5.8, 18, 43.3, and 96.3 kWh. It is hoped that with this research can be used by policymakers to increase the use of renewable energy, particularly solar energy, and contribute to achieving the 2030 Sustainable Development Goals (SDGs 2030) numbers 7 and 11, which focus on clean energy and sustainable cities and communities. Additionally, this research hopefully can contribute to urban planning for carbon neutrality by providing a more detailed understanding of PV and Battery capacity in the area with absence of detailed data information. It is also hoped that inclusive PV utilization can be planned not only by the government but also by building owners by understanding the optimal PV and battery capacity in every household.