A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

Yang, Hong-Tzer; Huang, Chao-Ming; Huang, Yann-Chang; Pai, Yi-Shiang

doi:10.1109/tste.2014.2313600

Cited by 421 publications

(185 citation statements)

References 14 publications

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“…To reduce the uncertainty of solar power caused by different weather patterns, studies that combine weather research and forecasting models have increased gradually in recent years [38][39][40]. In these related studies, weather classification is conducted as a pre-processing step for short-term solar forecasting to achieve better prediction accuracy than the same methods using a single simple uniform model for all weather conditions [41][42][43]. According to the existing achievements in solar forecasting studies, weather status pattern recognition and classification approaches have proven to be an effective way to increase the accuracy of forecasting results, especially for day-ahead forecasting.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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“…Furthermore, in order to define the accuracy of the prediction, some error indexes are introduced to evaluate the performances of the forecasting models. Some of these definitions come from statistics while others originate from regulatory authority for market issues [3,5,14,15]. The errors introduced by ANNs and physical methods sometimes are already too high for electricity market and RES imbalance issues.…”

Section: Open Accessmentioning

confidence: 99%

“…The idea is to combine different models with unique features to overcome the single negative performance and finally improve the forecast [15]. Hybrid systems combine different techniques ("paradigms") to overcome weaknesses and gain strengths.…”

Section: Energy Forecast Modelsmentioning

confidence: 99%

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

et al. 2015

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Abstract:The main purpose of this work is to lead an assessment of the day ahead forecasting activity of the power production by photovoltaic plants. Forecasting methods can play a fundamental role in solving problems related to renewable energy source (RES) integration in smart grids. Here a new hybrid method called Physical Hybrid Artificial Neural Network (PHANN) based on an Artificial Neural Network (ANN) and PV plant clear sky curves is proposed and compared with a standard ANN method. Furthermore, the accuracy of the two methods has been analyzed in order to better understand the intrinsic errors caused by the PHANN and to evaluate its potential in energy forecasting applications.

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“…It is composed of an input layer (PV power), a pattern layer, a summation layer and an output layer (duty cycle that is necessary to control the DC-DC converter to track the MPP [37][38][39]. Many papers have highlighted that, the ANN algorithm is a result of a compromise between rapidity in transient regime and stability in steady-state, even if its effectiveness has been proven and particularly in varying environmental conditions.…”

Section: Ann (Artificial Neural Network)mentioning

confidence: 99%

Relevance of the P & O MPPT Technique in an Original PV-Powered Water Pumping Application

Aissou¹,

Jacques²,

Mokrani³

et al. 2015

JEPE

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Many papers exploiting the various MPPT (maximum power point tracking) techniques in PV (photovoltaic) applications, from the simple to the most complicated, can be found in literature. However, these techniques may not always be easy to implement in industrial applications. The main challenge of this paper is to model and implement the P & O (perturb and observe) algorithm in a low-cost PV-powered pumping system. To that end, a comparative investigation of the performance characteristics of the most popular MPPT methods, such as FOCV (fractional open circuit voltage), FSCC (fractional short circuit current), FLC (fuzzy logic control), ANN (artificial neural network) and INC (incremental conductance) is presented. This analysis is helpful to highlight the relevance of the P & O technique taking better account of complexity, difficulty of implementation and cost considerations in water pumping applications. The targeted PV-powered pumping system is based on a single-phase induction motor supplied by a three-phase inverter controlled by the DTC (direct torque control) technique. This stand-alone PV system is dedicated to water pumping, especially in rural areas that have no access to national grids but have sufficient amount of solar radiation. Simulation modeling (Matlab/Simulink) and experimental results are presented to demonstrate the relevance of the system.

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A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output

Cited by 421 publications

References 14 publications

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output

Relevance of the P & O MPPT Technique in an Original PV-Powered Water Pumping Application

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