Solar Power Prediction via Support Vector Machine and Random Forest

Yen, Chih-Feng; Hsieh, He-Yen; Su, Kuan-Wu; Yu, M.; Leu, Jenq‐Shiou

doi:10.1051/e3sconf/20186901004

Cited by 20 publications

(11 citation statements)

References 13 publications

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“…The developed model, focusing on characteristics of electric load sequence as stability and flexibility sequence, can help systems to balance power supply and demand, to avoid possible catastrophes, to rationally allocate resources, and to capture trends in power system loads. In studies conducted in East-Asian countries with a time length from 6 months to 3 years, the obtained RMSE values varied from 0.086 to 1.39 [17,18,21,22]. The number of forecasting parameters was five, including the dew point and wind speed.…”

Section: Discussionmentioning

confidence: 99%

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Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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Section: Discussionmentioning

confidence: 99%

“…In [17], the Support Vector Machines (SVM) and Random Forest (RF) models were applied to forecast solar irradiance using weather parameters, such as temperature, humidity, rainfall and wind speed. In this work, the authors utilize SVM and RF models to predict individual PV generator output and compare their performances.…”

Section: Related Workmentioning

confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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“…This algorithm is concisely employed for data analysis and predicting due to its simplicity [53]- [55]. Figure 1 illustrates the steps for creating a random forest classifier [56]. In the working steps of this algorithm, randomisation is added to the proposed model as the trees grow.…”

Section: Methodsmentioning

confidence: 99%

Null-values imputation using different modification random forest algorithm

Mijwil

Abdulqader²,

Ali³

et al. 2023

IJ-AI

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<p><span lang="EN-US">Today, the world lives in the era of information and data. Therefore, it has become vital to collect and keep them in a database to perform a set of processes and obtain essential details. The null value problem will appear through these processes, which significantly influences the behaviour of processes such as analysis and prediction and gives inaccurate outcomes. In this concern, the authors decide to utilise the random forest technique by modifying it to calculate the null values from datasets got from the University of California Irvine (UCL) machine learning repository. The database of this scenario consists of connectionist bench, phishing websites, breast cancer, ionosphere, and COVID-19. The modified random forest algorithm is based on three matters and three number of null values. The samples chosen are founded on the proposed less redundancy bootstrap. Each tree has distinctive features depending on hybrid features selection. The final effect is considered based on ranked voting for classification. This scenario found that the modified random forest algorithm executed more suitable accuracy results than the traditional algorithm as it relied on four parameters and got sufficient accuracy in imputing the null value, which is grown by 9.5%, 6.5%, and 5.25% of one, two and three null values in the same row of datasets, respectively.</span></p>

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“…The proposed MLFFNN is compared with some other previous published methods which are used for the shortterm power prediction and presented in ref. [11], [14], [16], [38]. This comparison is presented in Fig.…”

Section: Trained Mlffnn Effectiveness and Generalizationmentioning

confidence: 99%

“…On the other hand, methods established on machine learning such as SVM and NNs were proposed for predicting the PV power. In [11], the proposed method predicted one-hour ahead of PV output power based on SVM and random forest using various weather data such as temperature, humidity, rainfall, and wind speed. The NN has the properties that it can approximate any function and its ability of generalization under different conditions [12], [13].…”

mentioning

confidence: 99%

Machine Learning Method for Solar PV Output Power Prediction

Sharkawy

Ali

Mousa

et al. 2022

SVU-International Journal of Engineering Sciences and Applicati

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To deal with the challenges of the solar photovoltaic (PV) energy source due to the continuous variations of the climatic conditions such as temperature and solar radiation, output power prediction is one of the most important research trends nowadays. In this paper, a multilayer feedforward neural network (MLFFNN) is executed to foresee the power for a solar PV power station. The MLFFNN employs the temperature and radiation as the inputs and the power as the output. For training and testing the MLFFNN, data of 6 days are acquired from a real PV power station in Egypt. The first five days are employed to train the MLFFNN using Levenberg-Marquardt (LM) algorithm. While the data of the sixth day, are used to check the effectiveness and the generalization ability of the trained MLFFNN. The results prove that the trained MLFFNN is working very well and efficient to predict the PV output power correctly.

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Solar Power Prediction via Support Vector Machine and Random Forest

Cited by 20 publications

References 13 publications

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Null-values imputation using different modification random forest algorithm

Machine Learning Method for Solar PV Output Power Prediction

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