Automatic hourly solar forecasting using machine learning models

Yagli, Gokhan Mert; Yang, Di; Srinivasan, Dipti

doi:10.1016/j.rser.2019.02.006

Cited by 207 publications

(75 citation statements)

References 61 publications

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“…A comprehensive review of applications of deep learning approaches on machine health monitoring tasks is presented in [11]. Machine learning for hourly solar forecasting application is proposed in [12]. Reference [13] reviews the ML models that are used for condition monitoring in wind turbines.…”

Section: Introductionmentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.

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Section: Introductionmentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

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“…Existing methods can be roughly divided into three categories: the numerical weather prediction (NWP), the image-based technique, and the statistical methods. NWP methods generate solar irradiance with hundreds of meteorological parameters [4]; It is a versatile approach for 6-48-h-ahead forecasting [5]. However, NWP model is based on dynamic equations of the atmospheric states computed with a fixed resolution, usually at the regional and national scale, not site-specific.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning has a strong nonlinear fitting capability, but at the same time, it has higher requirements for data volume. An overview of machine learning-based solar forecasting approaches has been provided in the literature [18].…”

Section: Introductionmentioning

confidence: 99%

Solar Power Forecasting Based on Domain Adaptive Learning

et al. 2020

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Solar power forecasting is critical to ensure the safety and stability of the power grid with high photovoltaic power penetration. Machine learning methods are compelling in solar forecasting. These methods can capture the complex coupling relationship between different meteorological factors without physical modeling. Most of the existing machine learning based forecasts follow the batch learning manner. Once the training is completed, the structure and parameters of the model are usually no longer adjusted. However, the climate is complex and dynamic. It is difficult for a fixed model to adapt to the climate characteristics of different regions or periods. Therefore, an online domain adaptive learning approach is proposed in this paper. Knowledge can be selectively accumulated or forgotten in its iterative process. As weather changes, the model can dynamically adjust its structure to adapt to the latest weather conditions. Unlike existing adaptive iterative methods, the proposed adaptive learning approach does not rely on the labels of the test data in the updating process. Experiments show that this method can effectively track changes in data distribution and obtain reliable prediction results. INDEX TERMS Solar power forecasting, adaptive learning, neural networks, ensemble learning.

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“…The proposed model with 15 min data showed superior performance (MAPE of 1.8852%) compared to other cases and models. Yagli et al (2019) evaluated the performance of 68 ML algorithms for three sky conditions, seven locations, and five different climate zones. All algorithms implemented without any modifications for a fair comparison.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

et al. 2019

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The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kW ac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R 2) performance metrics, respectively, compared to the Persistence prediction model of literature.

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Automatic hourly solar forecasting using machine learning models

Cited by 207 publications

References 61 publications

A survey of big data and machine learning

A survey of big data and machine learning

Solar Power Forecasting Based on Domain Adaptive Learning

Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction

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