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Teguh Hady Ariwibowo

Program Studi Sistem Pembangkit Energi, Departemen Teknik Mekanika Dan Energi, Politeknik Elektronika Negeri Surabaya Jl. Raya ITS, Sukolilo Surabaya 60111 Indonesia

Author-ID : 577109

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Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks Rohman, Budiman Putra Asmaur; Hilman, Catur; Tridianto, Erik; Ariwibowo, Teguh Hady
EMITTER International Journal of Engineering Technology Vol 6, No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Solar power is a renewable energy interest many researchers around the world to be explored for human life beneficial especially for electric power generation. Photovoltaic (PV) is one of technology developed massively to exploit the solar power for this purpose. However, its performance is very sensitive to environmental condition such as solar irradiance, weather, and climatic behavior. Thus, the hybrid power generation systems are developed to solve this output uncertainty problem. To support this such hybrid system, this paper proposes an ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power management. The object of this research is the PV systems equipped with two axes automated solar tracking with peak power 10Wp. The proposed ensemble forecaster model employs four multi-layer perceptron neural networks with two hidden layers as base forecasters while the input number of historical data is varied in order to exploit the forecaster diversity. The final prediction is calculated both by conventional averaging and simple weighting optimized by the least square fitting technique. According to the research results, the both proposed approaches provide low error rate. Moreover, in term of comparison, the ensemble model with averaging combining technique gives the highest accuracy comparing to the other ensemble and conventional neural network structures.

Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks Budiman Putra Asma'ur Rohman; Catur Hilman; Erik Tridianto; Teguh Hady Ariwibowo
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Solar power is a renewable energy interest many researchers around the world to be explored for human life beneficial especially for electric power generation. Photovoltaic (PV) is one of technology developed massively to exploit the solar power for this purpose. However, its performance is very sensitive to environmental condition such as solar irradiance, weather, and climatic behavior. Thus, the hybrid power generation systems are developed to solve this output uncertainty problem. To support this such hybrid system, this paper proposes an ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power management. The object of this research is the PV systems equipped with two axes automated solar tracking with peak power 10Wp. The proposed ensemble forecaster model employs four multi-layer perceptron neural networks with two hidden layers as base forecasters while the input number of historical data is varied in order to exploit the forecaster diversity. The final prediction is calculated both by conventional averaging and simple weighting optimized by the least square fitting technique. According to the research results, the both proposed approaches provide low error rate. Moreover, in term of comparison, the ensemble model with averaging combining technique gives the highest accuracy comparing to the other ensemble and conventional neural network structures.

Numerical Analysis of Wave Load Characteristics on Jack-Up Production Platform Structure Using Modified k-ω SST Turbulence Model Gilang Muhammad; Arini, Nu Rhahida; Ilman, Eko Charnius; Ariwibowo, Teguh Hady
EMITTER International Journal of Engineering Technology Vol 11 No 1 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i1.806

One of the important stages in the offshore structure design process is the evaluation of the marine hydrodynamic load in which the structure operates, this is to ensure an appropriate design and improve the safety of the structure. Therefore, accurate modeling of the marine environment is needed to produce good evaluation data, one of the methods that can accurately model the marine environment is through the Computational Fluid Dynamic (CFD) method. This research aims to analyze the ocean wave load of pressure and force characteristics on the jack-up production platform hull structure using the (CFD) method. The foam-extend 4.0 (the fork of the OpenFOAM) software with waveFoam solver is utilized to predict the free surface flow phenomena as its capability to predict with accurate results. The Reynold Averaged Navier Stokes (RANS) turbulence model of k-ω SST is applied to predict the turbulence effect in the flow field. Five variations of incident wave direction type are carried out to examine its effect on the pressure and force characteristics on the jack-up production platform hull. The wave model shows inaccurate results with the decrease in wave height caused by excessive turbulence in the water surface area. Excessive turbulence levels can be overcome by incorporating density variable and buoyancy terms based on the Standard Gradient Diffusion Hypothesis (SGDH) into the turbulent kinetic energy equation. The k-ω SST Buoyancy turbulence model shows accurate results when verified to predict wave run-up and horizontal force loads on monopile structures. Furthermore, test results of the wave load on the jack-up production platform hull structure shows that the most significant wave load is obtained in variations with the wave arrival direction relatively opposite to the platform wall. Especially in the direction of 90° because it also has the most expansive impact surface area. Meanwhile, the lower wave load is obtained in variations 45° and 135°, which have the relatively oblique direction of wave arrival to the surface.

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