Renewable energy analysis in indigenous communities using bottom-up demand prediction

Fathollahzadeh, Mohammad Hassan; Speake, Andrew; Tabares-Velasco, Paulo Cesar; Khademian, Zoheir; Fight, Lisa Lone

doi:10.1016/j.scs.2021.102932

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…This research used System Advisor Model (SAM), an open source software developed by National Renewable Energy Lab (NREL) [10], for the techno-economic analysis [11]. Through the techno-economic analyses, the viability of PV and PV+EV projects were analyzed by 1) collecting data, and 2) modeling and simulating transition scenarios.…”

Section: Application Of System Advisor Model (Sam)mentioning

confidence: 99%

Potentials of Rooftop Photovoltaics Combined with Electric Vehicles for Decarbonization in Korean Cities

Soowon¹,

Chang²,

Cho³

et al. 2022

Preprint

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Section: Application Of System Advisor Model (Sam)mentioning

confidence: 99%

Potentials of Rooftop Photovoltaics Combined with Electric Vehicles for Decarbonization in Korean Cities

Soowon¹,

Chang²,

Cho³

et al. 2022

Preprint

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“…The aim of increasing renewable energy demand coverage is increasingly being applied all over the world, especially in small communities [11]. In order to increase the share of renewable energy demand coverage in El Hierro island, the addition of a new photovoltaic power system and a new battery storage system was proposed [12].…”

Section: Introductionmentioning

confidence: 99%

Increasing Demand Covered by Renewable Energy in El Hierro Island

Briongos,

Platero Gaona,

Sánchez-Fernández

2023

Sustainability

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The power system of the El Hierro island comprises a wind farm, a pumped storage hydropower plant and a diesel power plant. Its operational history shows that the renewable energy participation in the energy mix is approximately 50%. In order to increase this participation, the installation of residential photovoltaic generation and battery storage has been considered. In this paper, the operation of the whole system is analyzed by simulation. Simulations consider historical data of electric supply and demand in addition to meteorological data to obtain the wind and solar power production. The simulation tool has been tuned to successfully reproduce past system performance. From the simulations, the effect of adding new renewable generation and storage has been assessed. Also, the effect of a different operation strategy has been analyzed. From the results of these simulations, adding batteries has a negligible effect in increasing renewable energy participation. By contrast, adding solar power provides an increase in renewable energy participation. However, eliminating the requirement of 50% of synchronous generation has the largest effect on increasing renewable energy participation in the energy mix.

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“…Several approaches for predicting RE have been grown over time, all of which have emphasized on the efficiency of estimation techniques with no or small concern for the environmental conditions. Renewable energy can efficiently cut fossil energy use, minimize pollution, and promote the healthy growth of the social economy (Fathollahzadeh et al, 2021; Jia et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms

et al. 2022

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This work focused on the prediction of generation of renewable energy (solar and wind) using the machine learning ML algorithms. Prediction of generation are very important to design the better microgrids storage. The various ML algorithms are as logistic regression LR and random forest RA and the ARIMA, time series algorithms. The performance of each algorithm is evaluated using the mean absolute error, mean squared error, root mean squared error, and mean absolute percentage error. The MAE value for the ARIMA (0.06 and 0.20) model for solar and wind energy is very less as compared to RF (15.65 and 61.73) and LR (15.78 and 54.65) of solar and wind energy. Same with MSE and RMSE, the MSE and RMSE value for the ARIMA of solar energy model obtained is 0.01 and 0.08 and wind energy is 0.07 and 0.27 respectively. Comparative analysis of all of these matrices of each algorithm for both the dataset, we concluded that the ARIMA model is best fit for the forecasting of solar energy and wind energy.

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Renewable energy analysis in indigenous communities using bottom-up demand prediction

Cited by 10 publications

References 27 publications

Potentials of Rooftop Photovoltaics Combined with Electric Vehicles for Decarbonization in Korean Cities

Potentials of Rooftop Photovoltaics Combined with Electric Vehicles for Decarbonization in Korean Cities

Increasing Demand Covered by Renewable Energy in El Hierro Island

Analysis for the prediction of solar and wind generation in India using ARIMA, linear regression and random forest algorithms

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