Hourly PV production estimation by means of an exportable multiple linear regression model

Trigo-González, Mauricio; Batlles, F.J.; Alonso-Montesinos, Joaquín; Ferrada, Pablo; Sagrado, José del; Martínez-Durbán, M.; Cortés-Carmona, Marcelo; Portillo, Carlos; Marzo, Aitor

doi:10.1016/j.renene.2018.12.014

Cited by 41 publications

(9 citation statements)

References 28 publications

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“…The linear regression model is explained below. A comprehensive study on the use of linear regression along with an improved model for hourly forecasting can be found in [28].…”

Section: Of 15mentioning

confidence: 99%

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

et al. 2020

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The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.

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“…The linear regression model is explained below. A comprehensive study on the use of linear regression along with an improved model for hourly forecasting can be found in [28].…”

Section: Of 15mentioning

confidence: 99%

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

et al. 2020

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“…The energy produced by the PV plant is intermittent and is highly dependent on a number of variables, such as solar irradiance, temperature and other atmospheric parameters (e.g., humidity and cloud coverage), as well as age of the equipment and operational condition [64]. According to the literature, there are numerous applications of multiple linear regression (MLR) models for energy production forecasting, such as hourly PV production estimation [65]. In this context, an MLR model was adopted, to predict the PV production (ŷ), considering the relation among different variables (x i ):…”

Section: Methodological Approachmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

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European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

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“…In this work, the optimal number of intermediate neurons has been determined empirically as the minimal number of neurons for which estimation performance on a test set is satisfying [13], for more information see Refs. [15,23,55]. [13].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

et al. 2023

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Hourly PV production estimation by means of an exportable multiple linear regression model

Cited by 41 publications

References 28 publications

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

Big Data for Energy Management and Energy-Efficient Buildings

Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

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