Post-processing of solar irradiance forecasts from WRF model at Reunion Island

Diagne, Maïmouna; David, Mathieu; Boland, John; Schmutz, Nicolas; Lauret, Philippe

doi:10.1016/j.solener.2014.03.016

Cited by 87 publications

(26 citation statements)

References 15 publications

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“…Akarslan et al and Diagne et al applied linear filtering approach for solar forecasting. Akarslan et al converted extraterrestrial irradiance and ambient temperature in the form of 2‐D image, and correlation between different images was computed using multidimensional linear prediction filters.…”

Section: Solar Forecastingmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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Section: Solar Forecastingmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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“…To evaluate the accuracy between observed data from mega solar Gifu weather station with simulated data from WRF model and LSTM algorithm, the two of three equations from David et al [26] such as root mean square error (RMSE) and mean bias error (MBE) are used to measure forecast accuracy and expressed in W/m 2 . Meanwhile, the normalization is done concerning the mean ground measurement of solar radiation in the considered period.…”

Section: Error Metricsmentioning

confidence: 99%

Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu, Japan

Araujo

2020

Environ. Res. Commun.

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Three months comparison of hourly solar radiation forecasting from 1st January to 31st March 2017 between Weather Research and Forecasting (WRF) mesoscale model and Long short-term memory (LSTM) algorithm is presented in this study. One-way grid nesting technique of the WRF model is applied for the simulation with a six-hourly input dataset downloaded from the National Oceanic and Atmospheric Administration -National Operational Model Archive and Distribution System (NOMADS) website. Three years'data of solar radiation from 1st January 2014 to 31st December 2016 are used as input data for Long Short Term Memory (LSTM) algorithm to simulate solar radiation. The results show the root mean square error of the LSTM algorithm is 310 W m −2 higher compared to 210 W m −2 from the WRF model. The MBE and the nMBE of the WRF model are obtained positive value 96 W m −2 and 9% compared to −101 W m −2 and −9% of LSTM for 2160 h prediction. Meanwhile, the performance error percentage of WRF is 19% lower compared to 28% of LSTM for the nRMSE error metric. Although this study found that the WRF model performed better and lower error compared to the LSTM algorithm, however, it also recommends the LSTM algorithm configuration can be used for long-term prediction.

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“…statistical measures that are conventionally accepted as the key performance criteria in the solar forecasting literature (Cao and Cao, 2006;Zhang et al, 2015). Following Kleissl (2013) and Diagne et al (2014), the analysis is performed only for daylight periods. In this situation this corresponds to solar altitudes greater than ten degrees.…”

Section: Point Forecastingmentioning

confidence: 99%

Nonparametric short-term probabilistic forecasting for solar radiation

2016

Self Cite

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The current deep concerns on energy independence and global society's security at the face of climate change have empowered the new ''green energy" paradigm and led to a rapid development of new methodology for modeling sustainable energy resources. However, clean renewables such as wind and solar energies are inherently intermittent, and their integration into a electric power grid require accurate and reliable estimation of uncertainties. And, if probabilistic forecasting of wind power is generally well developed, probabilistic forecasting of solar power is still in its infancy. In this paper we propose a new data-driven method for constructing a full predictive density of solar radiance based on a nonparametric bootstrap. We illustrate utility of the new bootstrapped statistical ensembles for probabilistic one-hour ahead forecasting in Mildura, Australia. We show that the new approach delivers sharp and calibrated ensembles of one-hour forecasts, and is computationally inexpensive and easily tractable.

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Post-processing of solar irradiance forecasts from WRF model at Reunion Island

Cited by 87 publications

References 15 publications

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Performance comparison of solar radiation forecasting between WRF and LSTM in Gifu, Japan

Nonparametric short-term probabilistic forecasting for solar radiation

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