Very short term forecasting of solar radiation

Moreno-Munoz, A.; Rosa, Juan José González de la; Posadillo, R.; Bellido, Francisco J.

doi:10.1109/pvsc.2008.4922587

Cited by 40 publications

(18 citation statements)

References 13 publications

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“…It is probably the reliability factor that is most appreciated and used [23]. The MAE and RMSE are shown in Equations (8) and (9):…”

Section: Modeling and Resultsmentioning

confidence: 99%

“…Power system operators must ensure an accurate balance between electricity production and consumption at any time, and effective forecasting techniques have become important to prepare for the grid integration of renewable energy sources with this instability [5]. The solar power forecasting has following advantages: (1) the effective operation of the power grid [6], (2) the optimal management of the energy fluxes occurring into the solar system, (3) estimating the reserves, (4) scheduling the power system, (5) congestion management, (6) the optimal management of the storage with the stochastic production, (7) trading the produced power in the electricity market, and (8) reduction of the costs of solar power generation. Accurate predictions can not only contribute to the reduction of uncertainty in power generation forecasts, but also add to the stable operation of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Accurate predictions can not only contribute to the reduction of uncertainty in power generation forecasts, but also add to the stable operation of the system. In addition, it allows photovoltaic plant operators to avoid penalties that may arise from differences between forecasted and produced energy, and benefits from cost saving for energy consumers [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Choi¹,

Hur

2020

Energies

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As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.

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“…It is probably the reliability factor that is most appreciated and used [23]. The MAE and RMSE are shown in Equations (8) and (9):…”

Section: Modeling and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Choi¹,

Hur

2020

Energies

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“…The errors of solar irradiance forecast based on NWP vary significantly and depend on the different climate and dynamic motion of the atmosphere at the study location [10][11][12]. The statistical models include time series models [13,14], satellite data based models [15], sky images based models [16], ANN and wavelet analysis based models [17][18][19]. The statistical models based on satellite data and sky images detect the motion of cloud structures using motion vector fields.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

Wang

Shi³

et al. 2012

Energies

286

103

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Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions. OPEN ACCESS

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“…Solar irradiance is known to be intermittent and diffuse energy source which is highly variable and usually site dependent [1,2]. PV power generation is quite different from that associated with conventional energy sources.…”

Section: Introductionmentioning

confidence: 99%

Reliability evaluation of electric power systems with solar photovoltaic & energy storage

Koh

Wang

Tseng

et al. 2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Renewable portfolio standards (RPS) place an obligation on independent service operators (ISOs) to have a minimum energy generation mix from an approved renewable energy source, e.g. photovoltaic (PV). The variability of PV power output impacts on electric power system reliability and energy storage (ES) is one of several options for better grid reliability. This paper convolves Roy Billinton Test System (RBTS) generation and load model, considering PV and ES to yield system adequacy indices at hierarchical level 1 (HL1) via an analytical technique. Uniform Singapore Energy Price (USEP) from the Energy Market Company (EMC) Singapore and NTU rooftop PV irradiance data are incorporated as parts of annual electricity cost saving research. The analytical technique proposed can help ISOs as first cut analysis to evaluate the potential of PV & ES to benefit system reliability and ways to improve its potential applications.Index Terms-power system reliability, energy storage, energy management, photovoltaic

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Very short term forecasting of solar radiation

Cited by 40 publications

References 13 publications

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

Reliability evaluation of electric power systems with solar photovoltaic & energy storage

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Very short term forecasting of solar radiation

Cited by 40 publications

References 13 publications

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

Reliability evaluation of electric power systems with solar photovoltaic &amp; energy storage

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Product

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Reliability evaluation of electric power systems with solar photovoltaic & energy storage