scholarly journals Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

2021 ◽  
pp. 159-169
Author(s):  
Mohammed QASEM
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Clean Energy ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

According to the World Economic Outlook (WEO), the global demand for energy is presum- ably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a re- sult of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), rela- tive root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.

scholarly journals Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nicholas Kwarikunda ◽  
Zivayi Chiguvare
Keyword(s):  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Measured Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Clear Sky

Evaluation of the maximum solar energy potential of a given area for possible deployment of solar energy technologies requires assessment of clear sky solar irradiance for the region under consideration. Such localized assessment is critical for optimal sizing of the technology to be deployed in order to realize the anticipated output. As the measurements are not always available where they are needed, models may be used to estimate them. In this study, three different models were adapted for the geographical location of the area under study and used to estimate clear sky global horizontal irradiance (GHI) at three locations in the subtropical desert climate of Namibia. The three models, selected on the basis of input requirements, were used to compute clear sky GHI at Kokerboom, Arandis, and Auas. The models were validated and evaluated for performance using irradiance data measured at each of the sites for a period of three years by computing statistical parameters such as mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R2), normalized MBE, and normalized RMSE. Comparative results between modelled and measured data showed that the models fit well the measured data, with normalized root mean square error values in the range 4–8%, while the R2 value was above 98% for the three models. The adapted models can thus be used to compute clear sky GHI at these study areas as well as in other regions with similar climatic conditions.

scholarly journals Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Brahim Belmahdi ◽  
Mohamed Louzazni ◽  
Mohamed Akour ◽  
Daniel Tudor Cotfas ◽  
Petru Adrian Cotfas ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Mean Square ◽  
Solar Declination ◽  
Input Parameters ◽  
Statistical Metrics

This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature Tmin, maximal temperature Tmax, average temperature Taverage, difference temperature ΔT, ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.

scholarly journals Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
T. C. Chakraborty ◽  
Xuhui Lee
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

AbstractDiffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2 (root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

scholarly journals A High-Resolution Topographic Correction Method for Clear-Sky Solar Irradiance Derived with a Numerical Weather Prediction Model

2011 ◽  
Vol 50 (12) ◽  
pp. 2460-2472 ◽  
Author(s):  
José A. Ruiz-Arias ◽  
David Pozo-Vázquez ◽  
Vicente Lara-Fanego ◽  
Francisco J. Santos-Alamillos ◽  
J. Tovar-Pescador
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Numerical Weather

AbstractRugged terrain is a source of variability in the incoming solar radiation field, but the influence of terrain is still not properly included by most current numerical weather prediction (NWP) models. In this work, a downscaling postprocessing method for NWP-model solar irradiance through terrain effects is presented. It allows one to decrease the estimation bias caused by terrain shading and sky-view reduction, and to account for elevation variability, surface orientation, and surface albedo. The method has been applied to a case study in southern Spain using the Weather Research and Forecasting (WRF) mesoscale model with a spatial resolution of 30 arc s, resulting in disaggregated maps of 3 arc s. The validation was based on a radiometric network made of eight stations located in the Natural Park of Sierra Mágina over an area of roughly 30 × 35 km2 and 12 carefully selected cloudless days during a year. Three of the stations were equipped with tilted pyranometers. Their inclination and aspect were visually adjusted to the inclination and aspect of the local terrain and then carefully measured. For horizontal surface, the downscaled irradiance has proven to reduce the root-mean-square error of the WRF model by 20% to about 25 W m−2 in winter and autumn and 60 W m−2 in spring and summer. For tilted surface, downscaling to different spatial resolutions resulted in the best performance for 9 arc s, with root-mean-square error of 45% (57 W m−2) and a mean bias error close to zero.

scholarly journals Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 53
Author(s):  
Joohwan Sung ◽  
Sungmin Han ◽  
Heesu Park ◽  
Hyun-Myung Cho ◽  
Soree Hwang ◽  
...  
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Recurrent Neural Network ◽  
Joint Angle ◽  
Low Frequency ◽  
Measurement Unit ◽  
Mean Square ◽  
Overground Walking

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

scholarly journals IMPLEMENTASI METODE NEURAL NETWORK BACKPROPAGATION DALAM PREDIKSI INDEKS HARGA SAHAM GABUNGAN (IHSG)

SEMINASTIKA ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 79-85
Author(s):  
Okky Barus ◽  
Christopher Wijaya
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Composite Index ◽  
Mean Square

Pada era saat ini, Investasi saham di pasar modal merupakan aset yang sangat penting bagi beberapa golongan masyarakat dan juga bagi perusahaan. Dengan adanya investasi, secara langsung maupun tidak langsung dapat memberikan dampak bagi perusahaan maupun bagi masyarakat. Penelitian ini bertujuan untuk memprediksi Indeks Harga Saham Gabungan (IHSG) dengan indeks saham: Jakarta Composite Index (JKSE). Metode yang digunakan pada penelitian ini adalah Neural Network Backpropagation. Pengumpulan dataset melalui website finance.yahoo.com dengan periode 8 Mei 2018 sampai dengan 7 Mei 2021 sebanyak 757 data. Setelah melakukan proses pengolahan data, data yang tersisa adalah 724 data. Kemudian data akan dibagi menjadi 70% data training dan 30% data testing yang akan digunakan pada proses pengolahan data. Hasil pengujian menggunakan metode Neural Newtwork Backpropagation mendapatkan hasil terbaik menggunakan Kondisi ke-10 dengan nilai Root Mean Square Error (RMSE) senilai 0.010. Kemudian akan didapatkan hasil perbandingan antara harga Close aktual dengan harga Close prediksi dengan akurasi sebesar 63.06% yang dapat membantu dalam pengambilan keputusan para investor.

scholarly journals Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria

2021 ◽  
Vol 20 (2) ◽  
pp. 113-119
Author(s):  
Khaled Ferkous ◽  
Farouk Chellali ◽  
Abdalah Kouzou ◽  
Belgacem Bekkar
Keyword(s):  
Solar Radiation ◽  
Root Mean Square Error ◽  
Gaussian Process ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Horizontal Surface ◽  
Total Solar Radiation ◽  
Mean Square

Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

scholarly journals Prediction of quality attributes and maturity classification of pear fruit using laser imaging and Artificial Neural Network

Food Research ◽  
2021 ◽  
Vol 5 (S1) ◽  
pp. 144-151
Author(s):  
S.E. Adebayo ◽  
N. Hashim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optical Properties ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Square ◽  
Classification Models ◽  
Pear Fruit ◽  
Laser Imaging

In this study, the application of laser imaging technique was utilized to predict the quality attributes (firmness and soluble solids content) of pear fruit and to classify the maturity stages of the fruit harvested at different days after full bloom (dafb). Laser imaging system emitting at visible and near infra-red region (532, 660, 785, 830 and 1060 nm) was deployed to capture the images of the fruit. Optical properties (absorption ma and reduced scattering ms ʹ coefficients) at individual and combined wavelengths of the laser images of the fruit were used in the prediction and classifications of the maturity stages. Artificial neural network (ANN) was employed in the building of both prediction and classification models. Root mean square error of calibration (RMSEC), root mean square error of crossvalidation (RMSECV), correlation coefficient (r) and bias were used to test the performance of the prediction models while sensitivity and specificity were used to evaluate the classification models. The results showed that there was a very strong correlation between the ma and ms ʹ with pear development. This study had shown that optical properties of pears with ANN as prediction and classification models can be employed to both predict quality parameters of pear and classify pear into different (dafb) non-destructively.

scholarly journals Prediksi Permintaan Kargo pada Cargo Service Center Tangerang City Menggunakan Metode Gated Recurrent Unit

Repositor ◽  
2020 ◽  
Vol 2 (8) ◽  
Author(s):  
Rifky Ahmad Saputra
Keyword(s):  
Neural Network ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Recurrent Neural Network ◽  
Mean Square ◽  
Service Center ◽  
Gated Recurrent Unit

Pada saat ini persaingan bisnis dalam bidang layanan kargo khususnya di Indonesia semakin ketat. Terdapat beberapa perusahaan layanan kargo di Indonesia, salah satunya yaitu Cargo Service Center Tangerang City. Untuk mengantisipasi persaingan bisnis tersebut, Cargo Service Center Tangerang City harus dapat menentukan strategi manajemen usaha, baik dalam jangka menengah maupun jangka panjang. Salah satunya hal yang dapat dilakukan yaitu prediksi permintaan kargo. Pada Cargo Service Center Tangerang City terdapat data transaksi kargo mulai dari Januari 2016 hingga Septermber 2019, oleh karena itu dilakukanlah penelitian yaitu mengimplementasikan metode Gated Recurrent Unit untuk melakukan prediksi permintaan kargo. metode Gated Recurrent Unit merupakan model pengembangan dari Recurrent Neural Network yang biasa digunakan untuk melakukan prediksi pada data sekuens. Pengujian model prediksi dalam penelitian ini dilakukan dengan mencari nilai Root Mean Square Error terkecil dari beberapa percobaan. Hasil dari penelitian ini menunjukkan bahwa model cukup baik dalam melakukan prediksi permintaan kargo, namun terdapat beberapa hasil prediksi metode Gated Recurrent Unit yang masih belum maksimal mendekati nilai aktual misalnya pada nilai aktual yang berada di titik puncak.

scholarly journals ANALISA KOMPARASI NEURAL NETWORK BACKPROPAGATION DAN MULTIPLE LINEAR REGRESSION UNTUK PERAMALAN TINGKAT INFLASI

2019 ◽  
Author(s):  
Amrin Amrin
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Root Mean Square Error ◽  
Multiple Linear Regression ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation

Tingkat inflasi tidak dapat dianggap remeh dalam sistem perekonomian suatu negara dan pelaku bisnis pada umumnya. Jika inflasi dapat diramalkan dengan akurasi yang tinggi, tentunya dapat dijadikan dasar pengambilan kebijakan pemerintah dalam mengantisipasi aktivitas ekonomi di masa depan. Pada penelitian ini akan digunakan metode prediksi neural network backpropagation dan multiple linear regression untuk memprediksi tingkat inflasi bulanan di indonesia, selanjutnya membandingkan manakah yang terbaik dari kedua metode tersebut. Data inflasi yang digunakan bersumber dari Badan Pusat Statistik dari tahun 2006-2015, dimana 80% sebagai data training dan 20% sebagai data testing. Dari hasil analisis data yang dilakukan disimpulkan bahwa Performa model multiple linear regression lebih baik dibandingkan dengan metode neural network backpropagation dengan nilai mean absolute deviation (MAD) sebesar 0.0380, mean square error (MSE) sebesar 0.0023, dan nilai Root Mean Square Error (RMSE) sebesar 0.0481

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