Long-term solar generation forecasting

Alanazi, Mohana; Alanazi, Abdulaziz; Khodaei, Amin

doi:10.1109/tdc.2016.7519883

Cited by 18 publications

(6 citation statements)

References 13 publications

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“…Due to the variation in climate, difficulties occur in the generation of energy and trading the electrical energy to the power grid. Therefore, a multi-stage long term solar power generation has been predicted with the use of ensemble machine learning approaches [26].…”

Section: Applications Of Model Usedmentioning

confidence: 99%

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Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Babbar¹,

Lau²,

Thang³

2021

IJACSA

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The usage of renewable energy sources has increased manifolds in terms of electric utilities. From other nonconventional sources, solar energy has been seen as a promising and convenient source used around the globe. In terms of meeting the daily requirements, solar energy has huge potential to fulfil the world's demand. However, firstly the characteristic of solar energy is very unpredictable and intermittent due to variation of weather. Secondly, the optimization and the planning of smart grid effect the operation of PV system. Thus, prediction on the long horizon is needed to address this problem. Nevertheless, long term forecasting of solar power generation is deliberated as a challenging problem. Therefore, this paper proposes a 10 day ahead solar power forecasting using combination of linear and non-linear machine learning models. At first, the outputs are generated from Recurrent Neural Network (RNN), Support Vector Machine (SVM) and Autoregressive with exogenous variable (ARX). Later on, these three outputs are combined and are made as a strong classifier with the Adaptive boost (Adaboost) algorithm. The simulations were conducted on the data obtained from real PV plant. By the experimental results and discussion, it was endogenously concluded that the combination of all techniques with the Adaboost have increased the performances and showing the high accuracy as compare to the individual machine learning models. The hybrid Adaboost shows %MAPE 8.88, which proven high accuracy. While on the other hand, for the individual technique, RNN shows 10.88, SVM reveals 11.78 and ARX gives 13.00 of percentage MAPE. The improvement proves that combination of techniques performs better than individual models and proclaims the high accuracy.

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Section: Applications Of Model Usedmentioning

confidence: 99%

“…Latently, the data set went through lots of variant processes and training sessions which finally show the precision in the prediction. In [26], simple neural networks are used and %MAPE has been calculated. It has bought 9.97% accuracy.…”

Section: Comparison Of All Models Usedmentioning

confidence: 99%

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Babbar¹,

Lau²,

Thang³

2021

IJACSA

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“…Alanazi et al [3] proposed a different approach using Neural networks toolbox and Global Horizontal Irradiance (GHI) values as main factor for long term solar forecasting. The method involves a set of pre-and post-processes to be carried out before and after the forecast is obtained.…”

Section: A Predicting Solar Irradiancementioning

confidence: 99%

Assessing the Utility of Weather Data for Photovoltaic Power Prediction

Zafarani¹,

Eftekharnejad²,

Patel³

2018

Preprint

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Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of having access to weather information for solar power generation prediction and find weather information that can help best predict photovoltaic power.

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“…At present, several methods of PV electricity generation forecasting have been proposed. According to time span, previous studies can be divided into: (1) very short-term-seconds, minutes, or hours ahead [12], (2) short-term-one or several days ahead [13], and (3) mid-and long-term-one or several weeks, months, or years ahead forecasting [14]. The present research belongs to the short-term forecasting.…”

Section: Introductionmentioning

confidence: 99%

Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter

Meng

Song

2020

Sustainability

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North China is one of the country’s most important socio-economic centers, but its severe air pollution is a huge concern. In this region, precisely forecasting the daily photovoltaic power generation in winter is essential to improve equipment utilization rate and mitigate effects of power system on the environment. Considering the climatic characteristics of North China, the winter days are divided into three classifications. A forecasting model based on random forest algorithm is then designed for each classification. To evaluate its performance, the proposed model and three other methods are separately used to forecast the daily power generation at the Zhonghe PV station, which is located in the center of North China. Empirical results show that, because of its ability to reduce the risk of overfitting by balancing decision trees, the proposed model obtains mean absolute percentage errors as low as 2.83% and 3.89% for clear and cloudy days, respectively. For days in which weather conditions are unusual, forecasting errors are relatively large. On these days, enlarging training samples, performing subdivision, and imposing manual intervention can improve the forecasting precision. Generally, the proposed model is better than the other three methods for nearly all error evaluation indicators in each classification.

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Long-term solar generation forecasting

Cited by 18 publications

References 13 publications

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model

Assessing the Utility of Weather Data for Photovoltaic Power Prediction

Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter

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