A one-day-ahead photovoltaic array power production prediction with combined static and dynamic on-line correction

Gulin, Marko; Pavlović, Tomislav; Vašak, Mario

doi:10.1016/j.solener.2016.12.008

Cited by 24 publications

(7 citation statements)

References 35 publications

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“…Since ants move stochastically when looking for food in nature, a random walk is selected by using Equations (13) and (14).…”

Section: Ant Lion Optimization Algorithmmentioning

confidence: 99%

“…In these equations, cumsum represents the cumulative sum, n represents the maximum number of iterations, t is the step of random walk, r(t) is a stochastic function and rand represents a random number generated with a uniform distribution in the range of [0, 1]. Since each search space has a boundary, Equations (13) and (14) cannot be used directly to update the position of ants. In order to keep the random walk of ants in the search space, normalization is performed at each iteration by using Equation (15).…”

Section: Ant Lion Optimization Algorithmmentioning

confidence: 99%

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Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

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Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.

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“…Since ants move stochastically when looking for food in nature, a random walk is selected by using Equations (13) and (14).…”

Section: Ant Lion Optimization Algorithmmentioning

confidence: 99%

Section: Ant Lion Optimization Algorithmmentioning

confidence: 99%

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

2020

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“…Also, it is already proposed that the distributed PV systems can be diagnosed by continuously comparing the short-term power production predictions and the actual values. Subsequently, a mismatch indicates a probable problem in the system, e.g., due to unusual shading, dust on panels or contacts corrosion [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

et al. 2019

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Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%. INDEX TERMS Fuzzy theory, photovoltaic (PV), power generation forecasting, Wang-Mendel.

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“…The crux of the indirect method is to predict the solar radiation intensity of the PV installation site, predict the solar radiation intensity at a certain time, and substitute it into the corresponding output model, thus obtaining the predicted output power value of the PV system [ 4 ]. Direct prediction methods do not require solar irradiance data and can predict the power output of PV power generation systems in the next time period by using only the historical PV system data and public weather information [ 5 – 8 ]. Some studies have shown that the influence of meteorological factors on the output power of PV systems is significant.…”

Section: Introductionmentioning

confidence: 99%

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

Gao

Yang

Pan

2017

Computational Intelligence and Neuroscience

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Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

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A one-day-ahead photovoltaic array power production prediction with combined static and dynamic on-line correction

Cited by 24 publications

References 35 publications

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

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