Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms

Khosravi, Ali; Koury, R.N.N.; Machado, Luiz; Pabón, Juan José García

doi:10.1016/j.jclepro.2017.12.065

Cited by 176 publications

(72 citation statements)

References 27 publications

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“…This indicates that the proposed ANN for the estimated energy consumption provided good results with high accuracy. Figure 26 to 29 give different examples of the calculated relative error during different months that represent different seasons during the year. From these figures, it is clear that the proposed ANN estimated value increased or decreased from the measured value in a very small and limited error of around 2 × 10 .…”

Section: Relative Error (Accuracy Of Proposed Ann)mentioning

confidence: 99%

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ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance

2019

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Solar energy is considered the greatest source of renewable energy. In this paper, a case study was performed for a single-axis solar tracking model to analyze the performance of the solar panels in an office building under varying ambient temperatures and solar radiation over the course of one year (2018). This case study was performed in an office building at the College of Engineering at Shaqra University, Dawadmi, Saudi Arabia. The office building was supplied with electricity for a full year by the designed solar energy system. The study was conducted across the four seasons of the studied year to analyze the performance of a group of solar panels with the total capacity of a 4 kW DC system. The solar radiation, temperature, output DC power, and consumed AC power of the system were measured using wireless sensor networks (for temperature and irradiance measurement) and a signal acquisition system for each hour throughout the whole day. A single-axis solar tracker was designed for each panel (16 solar panels were used) using two light-dependent resistors (LDR) as detecting light sensors, one servo motor, an Arduino Uno, and a 250 W solar panel installed with an array tilt angle of 21 • . Finally, an artificial neural network (ANN) was utilized to estimate energy consumption, according to the dataset of AC load power consumption for each month and the measurement values of the temperature and irradiance. The relative error between the measured and estimated energy was calculated in order to assess the accuracy of the proposed ANN model and update the weights of the training network. The maximum absolute relative error of the proposed system did not exceed 2 × 10 −4 . After assessment of the proposed model, the ANN results showed that the average energy in the region of the case study from a 4 kW DC solar system for one year, considering environmental impact, was around 8431 kWh/year. Sustainability 2019, 11, 6802 2 of 24 to develop previously discovered energy sources to eliminate the damage caused by non-renewable sources and to also compensate for the depletion of non-renewable resources in the future [1,2]. Solar energy has always been a promising source of renewable energy. It continues to attract research into its optimal development and utilization as a result of the good response and remarkable developments in solar energy systems to date. There has been a consistent, significant increase in the number of researchers investigating photovoltaic (PV) solar cells. In recent years, significant and remarkable developments have been observed in solar energy systems, which have led to a significant increase in the renewable energy market, with more solar energy systems being installed [3]. According to the REN21 (2018) Global Status Report, 2017 was a very important year in the field of solar energy, representing the increase of solar energy system installation above any other renewable energy technology. According to this report, at least 98 GW of grid-connected and off-grid solar energy systems were installe...

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Section: Relative Error (Accuracy Of Proposed Ann)mentioning

confidence: 99%

“…Accurate irradiation forecasting is essential for integrating this intermittent energy with the network. One of the most effective and popular methods for hourly solar forecasting is machine learning [28][29][30][31]. A new study used the deep neural network (DNN) method for forecasting the short-term irradiation of solar panels [32].…”

Section: Introductionmentioning

confidence: 99%

ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance

2019

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“…v max,j = δ(x max,j − x min,j ), (5) v min,j = δ(x min,j − x max,j ). (6) x max,j and x min,j are the minimum and maximum positions of the particle in the jth dimension, and δ is a constant between 0 and 1.…”

Section: Psomentioning

confidence: 99%

A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

et al. 2018

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The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting genetic algorithm II (NSGA II) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methods—namely, particle swarm optimization (PSO) algorithm and genetic algorithm (GA)—are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.

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“…Khosravi et al (2018) [11] estimated hourly solar radiation in Abu Musa island, Iran using machine learning algorithms. They compared the results of predicted hourly solar radiation from five different algorithms such as multilayer feedforward neural network (MLFFNN), radial basis function neural network (RBFNN), support vector regression (SVR), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS).…”

Section: Introductionmentioning

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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Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms

Cited by 176 publications

References 27 publications

ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance

ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance

A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

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

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