Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan

Fonseca, Joao Gari da Silva; Oozeki, Takashi; Takashima, Takumi; Koshimizu, Gentarou; Uchida, Yoshihisa; Ogimoto, Kazuhiko

doi:10.1002/pip.1152

Cited by 133 publications

(22 citation statements)

References 16 publications

(14 reference statements)

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“…The ANN may use solar radiation, ambient temperature and cloudiness as its inputs whereas power is given as output [24]. Past measurements of aggregated power and NWP forecast for cloudiness, radiation and irradiation would be used as inputs to an autoregressive model with exogenous input (ARX) building therefore a NARX recurrent neural networks.…”

Section: Fig 3 Generation and Proposed Explanatory Variablesmentioning

confidence: 99%

Short-Term Spanish Aggregated Solar Energy Forecast

Pérez-Mora

Canals

Martínez-Moll

2015

Advances in Computational Intelligence

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Abstract. This work presents and compare six short-term forecasting methods for hourly aggregated solar generation. The methods forecast one day ahead hourly values of Spanish solar generation. Three of the models are based on MLP network and the other three are based on NARX. The two different types of NN use to forecast the same NWP data, comprising solar radiation, solar irradiation and the cloudiness index weighted with the installed solar power for the whole country. In addition of the NWP data the models are fed with the aggregated solar energy generation in hourly step given by the System Operator.The results of the two types of NN are compared and discussed in the conclusions as much as the error variability along the day hours. The results obtained by the six methods are evaluated, concluding that the most accurate result is the one given by the developed NARX irradiance forecast method; achieving the lowest one day-ahead Mean Average Daily Error of 16.64%. IntroductionThe installation of power plants based on renewable energy sources have increased exponentially in the recent years [1]. The fuel-based energy generation price increase along with the subsidies promoting the construction of clean energies made the installation of renewables a profitable business for many particulars and companies [2]. The expansion of these technologies and the policies adopted by the Spanish national government to integrate them into the electrical power system has brought a fussy market situation. The integration of the electric energy generated by these power plants into electric power system is priority. According with the current legislation, the power generated by green sources is fed into the system in preferential order within the Spanish energy generation mix. This energy mix is result of a counter clockwise energy auction held one day-ahead.In order to always ensure the renewable energy acceptance into the system the energy bids [2] for such technologies are placed at the legal minimum, 0€ € /MWh and its retribution would be an addition of the final auction price and the generation subsidy agreed with the government. This fact may lead into situations where the energy demand is fewer than the energy generation by technologies offering their energy at the minimum price, being the final auction price 0€ € /MWh.

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Section: Fig 3 Generation and Proposed Explanatory Variablesmentioning

confidence: 99%

Short-Term Spanish Aggregated Solar Energy Forecast

Pérez-Mora

Canals

Martínez-Moll

2015

Advances in Computational Intelligence

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“…Affected by varying irradiance levels and different weather conditions, output power of PV plants has obvious intermittent volatility and randomness [3]. Currently, the large-scale integration of PV power into the existing energy supply structure has brought about a critical challenge for the power grid's security and economy.…”

Section: Introductionmentioning

confidence: 99%

A Seasonal Model Using Optimized Multi-Layer Neural Networks to Forecast Power Output of PV Plants

Lian

Han

et al. 2018

Energies

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Abstract:With the continuous increase of grid-connected photovoltaic (PV) installed capacity and the urgent demand of synergetic utilization with the other power generation forms, the high-precision prediction of PV power generation is increasingly important for the optimal scheduling and safe operation of the grid. In order to improve the power prediction accuracy, using the response characteristics of PV array under different environmental conditions, a data driven multi-model power prediction method for PV power generation is proposed, based on the seasonal meteorological features. Firstly, through the analysis of PV power characteristics in typical seasons and seasonal distribution of the weather factors, such as solar irradiance and ambient temperature, the influences of different weather factors on PV power prediction are studied. Then, according to the meteorology characteristics of Beijing, different seasons can be divided. The historical data corresponding to different seasons are acquired and then the seasonal PV power forecasting models are established based on optimized multi-layer back propagation neural network (BPNN), realizing the multi-model prediction of PV power. Finally, effectiveness of the seasonal PV power forecasting method is compared and validated. The performance analysis of the neural network forecasting model under typical seasonal conditions shows that the multi-model forecasting method based on seasonal characteristics of PV power generation is better than that of single power forecasting model for the whole year. The results show that the proposed method can effectively improve the power forecasting accuracy of PV power.

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“…In [19] the meteorological data provided by the meso-scale model (GPV-MSM), the weather forecast system of the Japan Meteorology Agency, both with the cloudiness and the extraterrestrial insolation data are numerically calculated, in order to perform a vector regression forecast one-hour-ahead, while in [20] the authors develop two models based on ANNs to forecast the output of a PV plant 24 h ahead starting from the insolation data. This model also uses data from GPV-MSM meso-scale model.…”

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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Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan

Cited by 133 publications

References 16 publications

Short-Term Spanish Aggregated Solar Energy Forecast

Short-Term Spanish Aggregated Solar Energy Forecast

A Seasonal Model Using Optimized Multi-Layer Neural Networks to Forecast Power Output of PV Plants

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

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