Solar production forecasting based on irradiance forecasting using artificial neural networks

Ioakimidis, Christos S.; López, Sergio Sánchez; Rycerski, Pawel; Šimić, Dragan

doi:10.1109/iecon.2013.6700491

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…In this work, ANN has proved to be a promising technique on this field, showing improved results while combined with GAs. The same conclusions about the use of ANNs were achieved by [10]. ANNs have also been successfully applied to the forecasting of other renewable sources based production types, such as the wind power, in [11].…”

Section: Solar Forecastingmentioning

confidence: 53%

Solar Intensity Characterization Using Data-Mining to Support Solar Forecasting

Pinto

Santos

Marques

et al. 2015

Distributed Computing and Artificial Intelligence, 12th International Conference

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Abstract. The increase of renewable based generation as alternative power source brings an added uncertainty to power systems. The intermittent nature of renewable resources, such as wind speed and solar intensity, requires the use of adequate forecast methodologies to support the management and integration of this type of energy resources. This paper proposes a clustering methodology to group historic data according to the data correlation and relevance for different contexts of use. Using the clustering process as a data filter only the most adequate data is used for the training process of forecasting methodologies. Artificial Neural Networks and Support Vector Machines are used to test and compare the quality of forecasts when using the proposed methodology to select the training data. Data from the Brazilian city of Florianópolis, Santa Catarina, has been used, including solar irradiance components and other meteorological variables, e.g. temperature, wind speed and humidity. Experimental findings show that using the proposed method to filter data used for training ANN and SVM achieved promising results, outperforming the approaches without clustering.

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Section: Solar Forecastingmentioning

confidence: 53%

Solar Intensity Characterization Using Data-Mining to Support Solar Forecasting

Pinto

Santos

Marques

et al. 2015

Distributed Computing and Artificial Intelligence, 12th International Conference

View full text Add to dashboard Cite

show abstract

“…In this work, ANN has proved to be a promising technique on this field, showing improved results while combined with GAs. The same conclusions about the use of ANNs were achieved by (Ioakimidis, et al, 2013) and (Singh et al, 2013). ANNs have also been successfully applied to the forecasting of other renewable sources based production types, such as the wind power, in (Hao et al, 2014).…”

Section: Solar Forecastmentioning

confidence: 64%

Data-Mining-based filtering to support Solar Forecasting Methodologies

Pinto

Marques

Sousa

et al. 2017

ADCAIJ

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“…The popularity of ANN-based methods is continuously increasing as a result of recent advancements in Deep Learning, and the continued development of computer hardware that supports its implementation. For instance, ANN [14] has been used to predict day-ahead solar power generation using input statistical features that were obtained from Global Horizontal Irradiance and temperature data. The method outperformed a Clear Sky physical method and obtained better results in the domain.…”

Section: N Gaf Number Of Gaf Images After Transformation O Tmentioning

confidence: 99%

Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

2020

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The operations of power systems are becoming more challenging on account of the high penetration of renewable power generation, including photovoltaic systems. One method for improving the power system operation involves making accurate forecasts of day-ahead solar irradiation, enabling operators to minimize uncertainty in managing the balance between generation and load. To overcome the limitations of Long Short-term Memory (LSTM) in a one-dimensional forecasting problem, this work proposes a novel method in forecasting solar irradiation by encoding time-series data into images using the Gramian Angular Field and the Convolutional LSTM (ConvLSTM) network. The pre-processed data become a five-dimensional input tensor that is perfectly suitable for ConvLSTM. The ConvLSTM network uses convolution operations in its input-to-state transition and state-to-state transition. The network thus enables time-series forecasting by a feature-rich approach, which ultimately provides competitive forecasting performance despite the use of a small dataset. The proposed method was evaluated in day-ahead solar irradiation forecasting using a univariate dataset of Global Horizontal Irradiation (GHI) data from Fuhai in Taiwan. The proposed method was resampled using 5×2-fold cross-validation, and assessed using the Wilcoxon signed-rank test to determine the statistical significance of the result. It outperformed benchmark methods such as Autoregressive Integrated Moving Average (ARIMA), Convolutional Neural Network cascaded with Long Short-term Memory (CNN-LSTM), and LSTM cascaded with a fully-connected (FC) network.

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Solar production forecasting based on irradiance forecasting using artificial neural networks

Cited by 11 publications

References 12 publications

Solar Intensity Characterization Using Data-Mining to Support Solar Forecasting

Solar Intensity Characterization Using Data-Mining to Support Solar Forecasting

Data-Mining-based filtering to support Solar Forecasting Methodologies

Day-Ahead Solar Irradiation Forecasting Utilizing Gramian Angular Field and Convolutional Long Short-Term Memory

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