Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Shi, Jie; Lee, Wei-Jen; Liu, Yongqian; Yang, Yongping; Wang, Peng

doi:10.1109/tia.2012.2190816

Cited by 664 publications

(300 citation statements)

References 6 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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“…In addition, a human-machine construct intelligence framework was proposed in [26] to determine the horizon year load for a long term load forecasting. Machine learning methods such as SVM and neural networks have been used in carrying out forecasting [27][28][29][30][31][32][33][34]. For example, Shi et al [28] developed a SVM-based model for one-day-ahead power output forecasting using the characteristics of weather classification.…”

Section: Related Work In Qosmentioning

confidence: 99%

Towards a Security Enabled and SOA-based QoS (for the Smart Grid) Architecture

Chrysoulas¹,

Pitropakis²

2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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QoS and Security features are playing an important role in modern network architecures. Dynamic selection of services and by extension of service providers are vital in today's liberalized market of energy. On the other hand it is equally important for Service Providers to spot the one QoS Module that offers the best QoS level in a given cost. Type of service, response time, availability and cost, consist a basic set of attributes that should be taken into consideration when building a concrete Grid network. In the proposed QoS architecture Prosumers request services based on the aforementioned set of attributes. The Prosumer requests the service through the QoS Module. It is then the QoS Module that seeks the Service Provider that best fits the needs of the client. The aforementioned approach is well supplemented with an in depth analysis on existing authentication and authorization protocols. The authors believe that QoS and security can work in parallel without adding extra burden in the Smart Grid infrastructure. This is feasible by building an in advance system for placing, scheduling, and assigning of the requests for energy consumption or production, thus decongesting the traffic in the whole network.

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“…Some of these models for PV plants were at first oriented to obtain solar radiation predictions [5][6][7]. Some works present models specifically dedicated to the hourly power generation forecasting in PV plants [8][9][10][11]. The most applied technique in these forecasting models is a specific soft-computing technique known as Artificial Neural Networks (ANNs) but some papers use simple physical methods [3,12,13].…”

Section: Open Accessmentioning

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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Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines

Cited by 664 publications

References 6 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

Towards a Security Enabled and SOA-based QoS (for the Smart Grid) Architecture

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

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