A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM

Wang, Sen; Sun, Yonghui; Zhou, Yan; Mahfoud, Rabea Jamil; Hou, Dongchen

doi:10.3390/en13010087

Cited by 30 publications

(9 citation statements)

References 35 publications

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“…Wang et al [1] used also only historical PV power output and numerical weather prediction for the short-term forecasting of PV power. One of the main differences between the study [6] and this study is the type of forecasting method, whereby Wang et al [6] proposed interval forecasting of PV power with lower and upper boundaries, while the predictive model of this study makes deterministic point forecasting.…”

Section: Introductionmentioning

confidence: 90%

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

Maitanova¹,

Telle²,

Hanke³

et al. 2020

Energies

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A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee-based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions.

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Section: Introductionmentioning

confidence: 90%

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

Maitanova¹,

Telle²,

Hanke³

et al. 2020

Energies

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“…Shrinking and encircling prey and spiral swimming are simultaneous, Therefore, when the individual whale is updated, there is a 50% probability of contraction encirclement and 50% probability of spiral swimming. The expression is shown as Formula (14). P(t + 1) = P best (t) − A w D b rand < 0.5 D b e br cos(2πr) + P best (t) rand ≥ 0.5 (14) rand is a random number between 0 and 1.…”

Section: Bubble Net Predationmentioning

confidence: 99%

“…The expression is shown as Formula (14). P(t + 1) = P best (t) − A w D b rand < 0.5 D b e br cos(2πr) + P best (t) rand ≥ 0.5 (14) rand is a random number between 0 and 1.…”

Section: Bubble Net Predationmentioning

confidence: 99%

“…The adverse impacts of PV power generation limit the improvement of the grid connection rate of PV power generation, and are not conducive to the application of clean energy. Accurate prediction of PV power is conducive to the safe operation of renewable energy power systems, and is beneficial for the application of clean energy [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Liu

Feng

et al. 2021

Symmetry

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Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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“…The work "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM" [1] by S. Wang et al proposed a novel hybrid model for short-term PV output power interval forecasting based on sample entropy, ensemble empirical mode decomposition (EEMD), and relevance vector machine (RVM). The PV output power sequences were decomposed into several intrinsic mode functions (IMFs) and residual components by EEMD.…”

Section: Brief Overview Of the Contributionsmentioning

confidence: 99%

Machine Learning for Energy Systems

2020

Self Cite

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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

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A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM

Cited by 30 publications

References 35 publications

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Machine Learning for Energy Systems

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