Fostering Rooftop Solar PV Investments Toward Smart Cities through e-SMART PV

Nurliyanti, Vetri; Ahadi, Khalif; Muttaqin, Ruchdi; Pranoto, Bono; Srikandi, Gea Prita; Irsyad, Muhammad Indra al

doi:10.1109/icsgsc52434.2021.9490406

Cited by 7 publications

(8 citation statements)

References 2 publications

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“…Studies estimating hydropower potentials were also numerous either at national coverage [22,23], provincial level, or specific sites [24,25]. For solar energy potential, Nurliyanti et al [9] created a webbased application to estimate energy potentials of a rooftop solar PV installed on a house.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Unfortunately, studies estimating the potential of floating PV in Indonesia are still limited. The main concerns of most existing studies were the ground-mounted PV panels and rooftop solar PV [6,9,[26][27][28]. Based on a simple multiplication of the entire size of the freshwater lake area and the average global horizontal irradiation (i.e., 4.8 kWh/ m2/ day) from the Solargis database, Silalahi et al [6] projected that the floating PV potentials in Indonesia might reach 50 GWp.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yet, solar energy developments often conflict with land uses and prices [7]. Recently, the government encourages Indonesian to invest in rooftop solar photovoltaic (PV) [8,9]. However, Gunawan, Alifia and Fraser [10] predicted that rooftop solar PV is inadequate for achieving renewable energy and emission reduction targets.…”

Section: Introductionmentioning

confidence: 99%

“…Indonesia aims to deploy more floating PV plants; however, studies analyzing solar energy potentials in Indonesia only focused on ground-mounted PV panels and rooftop solar PV [6,9] or the floating PV 1105 (2022) 012004 IOP Publishing doi:10.1088/1755-1315/1105/1/012004 2 potential at a specific site [20]. Only Untoro, Gunawan, Hidayanto, Perkasa and Wijaya [19] estimated the potentials of floating PV plants in Indonesia but their analysis was limited to 12 dams with surface area higher than 100 ha, which is suitable for a 100 MW floating PV plant.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Floating Photovoltaic - Hydropower Potential Utilization in Indonesia

Aminuddin

Pranoto

Irsyad

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Most countries mitigate climate alter impacts by deploying renewable energy to decarbonize their energy system. One of the emerging renewable energy options is the integration of floating photovoltaic (FPV) and hydropower. The FPV has higher efficiency and helps to reduce evaporation and offset hydropower production during the dry season. Conversely, hydropower can overcome intermittent solar production due to its flexibility. Our study aims to estimate the energy potentials from FPV plants and hydropower integrations in Indonesia. As result, the country has 5,807 potential reservoirs for FPV and 26 hydropower plants with a reservoir area with a potential utilization of more than 1 MW. Those sites are suitable for 3 GW floating power plants and 2.8 GW hydropower. And those sites can reduce CO2 emissions by 2,911,197 tonnes per year.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Floating Photovoltaic - Hydropower Potential Utilization in Indonesia

Aminuddin

Pranoto

Irsyad

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…According to the National Electricity General Plan or RUKN, Indonesia has established goals for its energy mix, including a proportion of Renewable Energy Technologies (RET) of 23% by 2025 and 31% by 2050, a reduction of greenhouse gas emissions of 29-41% by 2030, and a goal of net-zero emissions by 2060. Following those, numerous research on estimating national potential renewable energy have been conducted, such as hydro potential energy 1) , wind potential energy 2) , solar radiation 3) , Geothermal 4) , and a web-based tool for estimating a rooftop solar PV system's capacity to produce energy 5) .…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Forecasting with Weather Data using Deep Learning - Case Study in Baron Techno Park

Halidah,

Hesty,

Aji

et al. 2023

Evergreen

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In a microgrid with small-scale renewable sources, the unpredictable and highly variable nature of wind necessitates the adoption of reliable wind forecasting technologies. This study employs artificial neural networks (ANNs), specifically the Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which are classified as Deep Learning (DL) networks. These models integrate diverse weather data, such as wind speed, temperature, humidity, and atmospheric pressure, derived from actual measurements collected in Baron Techno Park, an isolated microgrid situated in the coastal region of Yogyakarta, Indonesia. For various scenarios, the root-mean-square error (RMSE) and mean absolute error (MAE) performances of the proposed ANN-based multivariable model are given and contrasted. Furthermore, it examines the impact of incorporating multiple local variables in contrast to solely relying on wind power, comparing against the persistence method. The findings reveal that the model incorporating a comprehensive set of weather data as inputs attains the lowest RMSE and MAE values. It is also can be concluded that additional weather data, even though they show almost no correlation to wind power in Baron Techno Park can improve short-term wind power prediction, with an improvement of 2.3% for every addition of weather parameter.

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