Machine Learning Algorithms in Forecasting of Photovoltaic Power Generation

Su, Di; Batzelis, Efstratios; Pal, Bikash

doi:10.1109/sest.2019.8849106

Cited by 33 publications

(22 citation statements)

References 15 publications

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“…They can be further divided into two main categories: artificial intelligence (AI) based and regression methods [26,27]. In the wide group of AI, machine learning (ML) techniques such as artificial neural networks (ANN) have been deeply investigated [28]. In particular, ANNs are more suitable compared with classical statistical methods when nonlinear and complicated correlation exists between the data and no prior assumption is formulated [29].…”

Section: Pv Forecast Modelsmentioning

confidence: 99%

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

et al. 2021

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Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

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Section: Pv Forecast Modelsmentioning

confidence: 99%

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

et al. 2021

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“…The previous statements were shown to hold for all time resolutions in wind power forecasting. In Reference [8], a comparison of numerous commonly used ensemble, ANN, and other ML techniques was performed for solar power forecasting. Random Forest (RF), an ensemble method, was found to exhibit the best performance.…”

Section: State-of-the-artmentioning

confidence: 99%

“…One can see that the employment of an ensemble technique involves the (continuously improving) prediction of some variable based on historical data, without knowledge of the physical model relating the inputs with the outputs. This is, in fact, the definition of machine learning (ML), and, as such, ensemble methods are often classified accordingly [8]. In recent years, there has been increased interest in the use of ensemble methods for power system applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation

et al. 2020

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A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., meteorological forecasts) and the corresponding local output (e.g., recorded power generation). Given a new case (with forecasted meteorological variables), the resulting power generation is forecasted. This is performed by calculating a KDE-based similarity index to determine a set of most similar cases from the historical dataset. Then, the outputs of the most similar cases are used to calculate an ensemble prediction. The method is tested using historical weather forecasts and recorded generation of a PV installation in Portugal. Despite only being given averaged data as input, the algorithm is shown to be capable of predicting uncertainties associated with high frequency weather variations, outperforming deterministic predictions based on solar irradiance forecasts. Moreover, the algorithm is shown to outperform a neural network (NN) in most test cases while being exceptionally faster (32 times). Given that the proposed model only relies on public locally-metered data, it is a convenient tool for DG owners/operators to effectively forecast their expected generation without depending on private/proprietary data or divulging their own.

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“…In [18], a comprehensive performance assessment among some of the most popular PV power forecasting methods are performed on a dataset. The forecasting methods used in this paper include Artificial Neural Networks and Intelligent Algorithms.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

et al. 2020

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The demand for renewable energy generation, especially photovoltaic (PV) power generation, has been growing over the past few years. However, the amount of generated energy by PV systems is highly dependent on weather conditions. Therefore, accurate forecasting of generated PV power is of importance for large-scale deployment of PV systems. Recently, machine learning (ML) methods have been widely used for PV power generation forecasting. A variety of these techniques, including artificial neural networks (ANNs), ridge regression, K-nearest neighbour (kNN) regression, decision trees, support vector regressions (SVRs) have been applied for this purpose and achieved good performance. In this paper, we briefly review the most recent ML techniques for PV energy generation forecasting and propose a new regression technique to automatically predict a PV system’s output based on historical input parameters. More specifically, the proposed loss function is a combination of three well-known loss functions: Correntropy, Absolute and Square Loss which encourages robustness and generalization jointly. We then integrate the proposed objective function into a Deep Learning model to predict a PV system’s output. By doing so, both the coefficients of loss functions and weight parameters of the ANN are learned jointly via back propagation. We investigate the effectiveness of the proposed method through comprehensive experiments on real data recorded by a real PV system. The experimental results confirm that our method outperforms the state-of-the-art ML methods for PV energy generation forecasting.

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Machine Learning Algorithms in Forecasting of Photovoltaic Power Generation

Cited by 33 publications

References 15 publications

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results

A Novel Ensemble Algorithm for Solar Power Forecasting Based on Kernel Density Estimation

Deep Learning with Loss Ensembles for Solar Power Prediction in Smart Cities

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