Solar radiation forecasting in the short- and medium-term under all sky conditions

Alonso-Montesinos, Joaquín; Batlles, F.J.

doi:10.1016/j.energy.2015.02.036

Cited by 57 publications

(22 citation statements)

References 32 publications

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“…According to the fluctuation degree and the average value of natural environment, this paper classifies the weather category into three types. The result is shown in Table 1 [17]. …”

Section: The Classification Of Weather Categorymentioning

confidence: 69%

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Wang¹,

Ran²,

Song³

et al. 2017

Journal of Electrical Engineering and Technology

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-Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

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“…According to the fluctuation degree and the average value of natural environment, this paper classifies the weather category into three types. The result is shown in Table 1 [17]. …”

Section: The Classification Of Weather Categorymentioning

confidence: 69%

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Wang¹,

Ran²,

Song³

et al. 2017

Journal of Electrical Engineering and Technology

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“…Results validated the effectiveness of VOF over the rest of two methods. Alonso‐Montesinos and Batlles presented short‐ and medium‐term solar irradiance predictor that predicted three solar components including beam, diffuse, and global irradiance under all sky conditions. The Heliosat‐2 method was utilized to estimate atmospheric attenuation and European Solar Radiation Atlas (ESRA) model to forecast three solar components.…”

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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“…Meanwhile, the normalization is done concerning the mean ground measurement of solar radiation in the considered period. In this study, the normalized RMSE (nRMSE) and the normalized MBE (nMBE) for error percentage given by equations (10) and (11), and expressed in % [27]. These four error metrics are defined as below;…”

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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Solar radiation forecasting in the short- and medium-term under all sky conditions

Cited by 57 publications

References 32 publications

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

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

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