scholarly journals Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5509 ◽  
Author(s):  
Foster Lubbe ◽  
Jacques Maritz ◽  
Thomas Harms
Keyword(s):  
Renewable Energy ◽  
Solar Radiation ◽  
Power Systems ◽  
Gaussian Process ◽  
Solar Power ◽  
Mean Squared Error ◽  
Gaussian Process Regression ◽  
Root Mean Squared Error ◽  
Radiation Data ◽  
Squared Error

The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m2. Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

scholarly journals Hybrid Wind–PV Frequency Control Strategy under Variable Weather Conditions in Isolated Power Systems

2020 ◽  
Vol 12 (18) ◽  
pp. 7750 ◽  
Author(s):  
Ana Fernández-Guillamón ◽  
Guillermo Martínez-Lucas ◽  
Ángel Molina-García ◽  
Jose-Ignacio Sarasua
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Control Strategy ◽  
Rotational Speed ◽  
Power Plants ◽  
Mean Squared Error ◽  
Frequency Control ◽  
Weather Conditions ◽  
Squared Error ◽  
Variable Weather

Over the last two decades, variable renewable energy technologies (i.e., variable-speed wind turbines (VSWTs) and photovoltaic (PV) power plants) have gradually replaced conventional generation units. However, these renewable generators are connected to the grid through power converters decoupled from the grid and do not provide any rotational inertia, subsequently decreasing the overall power system’s inertia. Moreover, the variable and stochastic nature of wind speed and solar irradiation may lead to large frequency deviations, especially in isolated power systems. This paper proposes a hybrid wind–PV frequency control strategy for isolated power systems with high renewable energy source integration under variable weather conditions. A new PV controller monitoring the VSWTs’ rotational speed deviation is presented in order to modify the PV-generated power accordingly and improve the rotational speed deviations of VSWTs. The power systems modeled include thermal, hydro-power, VSWT, and PV power plants, with generation mixes in line with future European scenarios. The hybrid wind–PV strategy is compared to three other frequency strategies already presented in the specific literature, and gets better results in terms of frequency deviation (reducing the mean squared error between 20% and 95%). Additionally, the rotational speed deviation of VSWTs is also reduced with the proposed approach, providing the same mean squared error as the case in which VSWTs do not participate in frequency control. However, this hybrid strategy requires up to a 30% reduction in the PV-generated energy. Extensive detailing of results and discussion can be also found in the paper.

Estimating Daily Solar Radiation Using Hargreaves Model in Eastern Malaysia

2014 ◽  
Vol 699 ◽  
pp. 564-569 ◽  
Author(s):  
Mohd Irwan Yusoff ◽  
Muhamad Irwanto ◽  
Safwati Ibrahim ◽  
Gomesh Nair ◽  
Syed Idris Syed Hassan ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Model Estimation ◽  
Estimation Model ◽  
Radiation Data ◽  
Squared Error ◽  
Hargreaves Model

This paper presents the forecasting of solar radiation in Kelantan, Eastern Malaysia for the year of 2011 using Hargreaves model. This estimation is based on latitude and daily minimum and maximum temperature in Kelantan. The measured and estimated solar radiation data were compared for the year 2011 and analyzed using coefficient of residual mass (CRM), root mean squared error (RMSE), coefficient of determination (R2) and percentage error (e). The results showed that the value ofCRMis 0.09, it indicates the tendency of the estimation model to under-estimate the measure solar radiation. Meanwhile, the value ofRMSEis 8.21% and the value ofR2is 0.8661, closed to 1 indicates that about 86.61% of the total variation is explained in the data. For thee, the value is 7.98%, it indicates that the model estimation is good.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Comparative study of the pencil-and-paper and digital formats of the Spanish DARS scale

2021 ◽  
pp. 1-21
Author(s):  
Elsa Arrua-Duarte ◽  
Marta Migoya-Borja ◽  
Igor Barahona ◽  
Lena C. Quilty ◽  
Sakina J. Rizvi ◽  
...  
Keyword(s):  
Rating Scale ◽  
Mean Squared Error ◽  
Intraclass Correlation ◽  
Test Validity ◽  
Wilcoxon Test ◽  
Digital Version ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Digital Format ◽  
Paper And Pencil

Abstract Objective: The Dimensional Anhedonia Rating Scale (DARS) is a novel questionnaire to assess anhedonia of recent validation. In this work we aim to study the equivalence between the traditional paper-and-pencil and the digital format of DARS. Methods: 69 patients filled the DARS in a paper-based and digital versions. We assessed differences between formats (Wilcoxon test), validity of the scales (Kappa and Intraclass Correlation Coefficients), and reliability (Cronbach’s alpha and Guttman’s coefficient). We calculated the Comparative Fit Index and the Root Mean Squared Error associated with the proposed one-factor structure. Results: Total scores were higher for paper-based format. Significant differences between both formats were found for three items. The weighted Kappa coefficient was approximately 0.40 for most of the items. Internal consistency was greater than 0.94, and the Intraclass Correlation Coefficient for the digital version was 0.95 and 0.94 for the paper-and-pencil version (F= 16.7, p < 0.001). Comparative Adjustment Index was 0.97 for the digital DARS and 0.97 for the paper-and-pencil DARS, and Root Mean Squared Error was 0.11 for the digital DARS and 0.10 for the paper-and-pencil DARS. Conclusion: The digital DARS is consistent in many respects to the paper-and-pencil questionnaire, but equivalence with this format cannot be assumed without caution.

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

Two-Run Genetic Programming for Predicting Slump Flow of Concrete

2014 ◽  
Vol 590 ◽  
pp. 321-325
Author(s):  
Li Chen ◽  
Chang Huan Kou ◽  
Kuan Ting Chen ◽  
Shih Wei Ma
Keyword(s):  
Nonlinear Systems ◽  
Genetic Programming ◽  
High Performance ◽  
Mean Squared Error ◽  
High Performance Concrete ◽  
Local Optimum ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Slump Flow ◽  
Low Dimensional

A two-run genetic programming (GP) is proposed to estimate the slump flow of high-performance concrete (HPC) using several significant concrete ingredients in this study. GP optimizes functions and their associated coefficients simultaneously and is suitable to automatically discover relationships between nonlinear systems. Basic-GP usually suffers from premature convergence, which cannot acquire satisfying solutions and show satisfied performance only on low dimensional problems. Therefore it was improved by an automatically incremental procedure to improve the search ability and avoid local optimum. The results demonstrated that two-run GP generates an accurate formula through and has 7.5 % improvement on root mean squared error (RMSE) for predicting the slump flow of HPC than Basic-GP.

scholarly journals Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America

2020 ◽  
Vol 12 (18) ◽  
pp. 3098
Author(s):  
Jongmin Park ◽  
Barton A. Forman ◽  
Rolf H. Reichle ◽  
Gabrielle De Lannoy ◽  
Saad B. Tarik
Keyword(s):  
North America ◽  
Brightness Temperature ◽  
Mean Squared Error ◽  
Transfer Model ◽  
Vegetation Types ◽  
Large Variability ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
L Band ◽  
Soil Hydraulic

L-band brightness temperature (Tb) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in Tb observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, Tb estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily Tb observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed Tb were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed Tb with a relatively low, domain-average bias (0.79 K (ascending) and −2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled Tb due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

scholarly journals Hybrid Load Forecasting Using Gaussian Process Regression and Novel Residual Prediction

2020 ◽  
Vol 10 (13) ◽  
pp. 4588 ◽  
Author(s):  
Cosmin Darab ◽  
Turcu Antoniu ◽  
Horia Gheorghe Beleiu ◽  
Sorin Pavel ◽  
Iulian Birou ◽  
...  
Keyword(s):  
Power Systems ◽  
Gaussian Process ◽  
Electricity Markets ◽  
Load Forecasting ◽  
Gaussian Process Regression ◽  
Influential Factors ◽  
Percentage Error ◽  
Weather Factors ◽  
Empirical Wavelet Transform ◽  
Utility Companies

Short-term electricity load forecasting has attracted considerable attention as a result of the crucial role that it plays in power systems and electricity markets. This paper presents a novel hybrid forecasting method that combines an autoregressive model with Gaussian process regression. Mixed-user, hourly, historical data are used to train, validate, and evaluate the model. The empirical wavelet transform was used to preprocess the data. Among the perturbing factors, the most influential predictors that were recorded were the weather factors and day type. The developed methodology is upgraded using a novel closed-loop algorithm that uses the forecasting values and influential factors to predict the residuals. Most performance indicators that are computed indicate that forecasting the residuals actually improves the method’s precision, decreasing the mean absolute percentage error from 5.04% to 4.28%. Measured data are used to validate the effectiveness of the presented approach, making it a suitable tool for use in load forecasting by utility companies.

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