A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning

Huertas‐Tato, Javier; Aler, Ricardo; Galván, Inés María; Rodríguez-Benítez, Francisco J.; Arbizu‐Barrena, Clara; Pozo-Vázquez, D.

doi:10.1016/j.solener.2019.11.091

Cited by 43 publications

(16 citation statements)

References 24 publications

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“…Nevertheless, some preliminary conclusions can be drawn from the study. Firstly, taking into account other widely used techniques from [ 45 ], the forecast error obtained in this work, in terms of the rMAE, is much smaller under short lead times (15 min), increasing until a similar value of the error is obtained under large lead times (6 h). A good performance under small forecast horizons is also obtained when comparing the results with [ 46 ] for a statistical AutoRegressive Integrated Moving Average (ARIMA) model, in terms of the MAE, obtaining a similar error to that of traditional RNNs, and a higher error with respect to a similar LSTM-based approach presented in [ 46 ], despite considering other inputs highly correlated with the irradiance.…”

Section: Resultsmentioning

confidence: 83%

Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants

Moreno

Santos

Martín

et al. 2021

Sensors

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Solar energy penetration has been on the rise worldwide during the past decade, attracting a growing interest in solar power forecasting over short time horizons. The increasing integration of these resources without accurate power forecasts hinders the grid operation and discourages the use of this renewable resource. To overcome this problem, Virtual Power Plants (VPPs) provide a solution to centralize the management of several installations to minimize the forecasting error. This paper introduces a method to efficiently produce intra-day accurate Photovoltaic (PV) power forecasts at different locations, by using free and available information. Prediction intervals, which are based on the Mean Absolute Error (MAE), account for the forecast uncertainty which provides additional information about the VPP node power generation. The performance of the forecasting strategy has been verified against the power generated by a real PV installation, and a set of ground-based meteorological stations in geographical proximity have been used to emulate a VPP. The forecasting approach is based on a Long Short-Term Memory (LSTM) network and shows similar errors to those obtained with other deep learning methods published in the literature, offering a MAE performance of 44.19 W/m2 under different lead times and launch times. By applying this technique to 8 VPP nodes, the global error is reduced by 12.37% in terms of the MAE, showing huge potential in this environment.

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Section: Resultsmentioning

confidence: 83%

Intra-Day Solar Power Forecasting Strategy for Managing Virtual Power Plants

Moreno

Santos

Martín

et al. 2021

Sensors

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“…Blending approaches are used to improve the regional forecast with support vector machines to forecast short-term solar radiation system. 21 These techniques can forecast in short-term horizons, and most models adopt manual hand-engineered feature selection. The energy output of the hybrid photovoltaic (PV)-wind renewable energy system is carried out using models like extra trees, ADA Boost, SVR, K-neighbors, Gaussian process, and multi-layer perceptron by incorporating the feature selection techniques in each model.…”

Section: Renewable Energy Forecastingmentioning

confidence: 99%

A holistic review on energy forecasting using big data and deep learning models

Devaraj¹,

Elavarasan

Shafiullah

et al. 2021

Int J Energy Res

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With the growth of forecasting models, energy forecasting is used for better planning, operation, and management in the electric grid. It is important to improve the accuracy of forecasting for a faster decision-making process. Big data can handle large scale of datasets and extract the patterns fed to the deep learning models that improve the accuracy than the traditional models and hence, recently started its application in energy forecasting. In this study, an in-depth insight is initially derived by investigating artificial intelligence (AI) and machine learning (ML) techniques with their strengths and weaknesses, enhancing the consistency of renewable energy integration and modernizing the overall grid. However, Deep learning (DL) algorithms have the capability to handle big data by capturing the inherent non-linear features through automatic feature extraction methods. Hence, an extensive and exhaustive review of generative, hybrid, and discriminative DL models is being examined for short-term, medium-term, and long-term forecasting of renewable energy, energy consumption, demand, and supply etc. This study also explores the different data decomposition strategies used to build forecasting models. The recent success of DL is being investigated, and the insights of paradoxes in parameter optimization during the training of the model are identified. The impact of weather prediction in the wind and solar energy forecasting is examined in detail. From the existing literatures, it has seen that the average mean absolute percentage error (MAPE) value of solar and wind energy forecasting is 10.29% and 6.7% respectively. Current technology barriers involved in implementing these models for energy forecasting and the recommendations to overcome the existing system barriers are identified. An indepth analysis, discussions of the results, and the scope for improvement are provided in this study including the potential directions for future research in the energy forecasting.

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“…There exists significant number of earlier works on deep learning based solar power prediction systems [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%