A compound of feature selection techniques to improve solar radiation forecasting

Castangia, Marco; Aliberti, Alessandro; Bottaccioli, Lorenzo; Macii, Enrico; Patti, Edoardo

doi:10.1016/j.eswa.2021.114979

Cited by 52 publications

(21 citation statements)

References 29 publications

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“…Whilst, Distributed Generation forecast module of RES, for instance, PV panel production, can take advantage from machine learning models (e.g. [37], [38]) or simulators (e.g. [39]) to perform realistic estimation on energy production in short and/or mid-term, even considering historical real-world trends as input (i.e.…”

Section: Proposed Real-time Management Schemamentioning

confidence: 99%

IoT-Enabled Real-Time Management of Smart Grids With Demand Response Aggregators

Estebsari

Mazzarino

Bottaccioli

et al. 2022

IEEE Trans. on Ind. Applicat.

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Integration of widely distributed small-scale Renewable Energy Sources like rooftop Photovoltaic panels and emerging loads like plug-in Electric Vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this paper, we present a developed real-time management schema based on Internet of Things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified Optimal Power Flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulationbased test bed and realised the performance of this approach in a real-like environment using real data of a network with residential buildings.

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Section: Proposed Real-time Management Schemamentioning

confidence: 99%

IoT-Enabled Real-Time Management of Smart Grids With Demand Response Aggregators

Estebsari

Mazzarino

Bottaccioli

et al. 2022

IEEE Trans. on Ind. Applicat.

Self Cite

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“…In the literature, significant performance differences from the different ML and DL applications in solar radiation forecasting were reported. Castangia et al [14] used five machine learning models based on Feedforward, Echo State, 1D-Convolutional, LSTM neural networks, and Random Forest (RF) method. They used in their study six parameters: the cloud cover, air temperature, relative humidity, dew point, wind bearing, and sunshine duration.…”

Section: Related Workmentioning

confidence: 99%

“…where cov is the covariance, σ x the standard deviation of one input feature, and σ y the standard deviation of the solar radiation feature (output). This technique allows determining the correlation of each meteorological measurement with solar radiation [14], correlations that can be different with the season. The prediction accuracy can be statistically evaluated using PCC as a metric; a larger PCC intuitively reflects a higher linear correlation between the predicted and true values [33,45].…”

Section: Feature Selectionmentioning

confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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“…LSTMs are designed to address long-term dependency issues. LSTM provides faster convergence for the training data and can identify long-term dependencies in the input data [ 123 ]. The LSTM module consists of 3 separate gates: forget gate, input gate, and output gate.…”

Section: Comparative Analysismentioning

confidence: 99%

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Ayyıldız

Erdoğan

Gümüş

2021

Computers in Biology and Medicine

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A compound of feature selection techniques to improve solar radiation forecasting

Cited by 52 publications

References 29 publications

IoT-Enabled Real-Time Management of Smart Grids With Demand Response Aggregators

IoT-Enabled Real-Time Management of Smart Grids With Demand Response Aggregators

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

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