Systematical Evaluation of Solar Energy Supply Forecasts

Ulbricht, Robert; Hahmann, Martin; Donker, Hilko; Lehner, Wolfgang

doi:10.1007/978-3-319-13290-7_9

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Photovoltaic (PV) power generation technology is widely used worldwide [5,6] due to its high performance, efficiency and low cost, which can bring environmental and economic benefits by integrating PV systems into the grid [7]. However, with the increasing capacity of PV power generation, the effective power output of PV power generation has prediction methods can be categorized into physical methods, statistical methods and combined methods [12,13]. Physical methods are based on weather forecasts and solar irradiance data for prediction, but the prediction accuracy of all such methods is limited due to the variability and suddenness of weather.…”

Section: Background and Motivationmentioning

confidence: 99%

Short‐term photovoltaic prediction based on CNN‐GRU optimized by improved similar day extraction, decomposition noise reduction and SSA optimization

Li,

Wang,

et al. 2024

IET Renewable Power Gen

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The accuracy of short‐term photovoltaic (PV) power prediction is crucial for maintaining power system stability and grid scheduling. Here, a short‐term PV power prediction framework is proposed considering combined weather similarity day screening, signal decomposition noise reduction and hybrid deep learning to realize PV power prediction. First, a combined meteorological similar day screening model is constructed to screen out historical days similar to the day, which reduces the number of training sets; Second, Synchrosqueezing Wavelet Transform is utilized to eliminate data noise. Third, a Convolution Neural Network‐Gate Recurrent Unit (CNN‐GRU) network is constructed to extract periodic and nonlinear features in the PV power generation data series and to capture the relationship features between PV power generation and meteorological factors to improve the prediction accuracy. Fourth, the Sparrow Search Algorithm is introduced to perform hyper‐parameter optimization of the CNN‐GRU network to accelerate the model convergence and improve the model training efficiency. Finally, this paper conducts simulation experiments and the experimental results show that the prediction method proposed in this paper can effectively improve the prediction accuracy of short‐term PV power compared to the baseline model, and the method proposed in this paper is superior to other conventional methods.

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Section: Background and Motivationmentioning

confidence: 99%

Short‐term photovoltaic prediction based on CNN‐GRU optimized by improved similar day extraction, decomposition noise reduction and SSA optimization

Li,

Wang,

et al. 2024

IET Renewable Power Gen

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“…The random variation is usually based on fluctuations observed in historical data for a selected period [15]. Depending on the data selected and used to train the model, two groups of approaches can be identified [16][17][18][19][20][21][22][23][24][25]: 3. Hybrid models: They represent any combination of two or more of the methods described above [17,25].…”

Section: Prediction Modelsmentioning

confidence: 99%

Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

Martins

Rodrigues

Henriques

2020

KEG

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Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance. Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance

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“…From the expression of the absolute value |e t |, most commonly used error definitions can be inferred [30] [31], such as:…”

Section: Diagnostic Indicatorsmentioning

confidence: 99%

PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast

Leva

Mussetta

Ogliari

2019

IEEE Trans. Ind. Electron.

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The employment of solar micro-converter allows a more detailed monitoring of the PV output power at the single module level; thus, machine learning techniques are capable to track the peculiarities of modules in the PV plants such as regular shadings. In this way it is possible to compare in real-time the day-ahead forecast power with the actual one in order to better evaluate faults or anomalous trends which might have occurred in the PV plant. This paper presents a method for an effective fault diagnosis; this method is based on the day-ahead forecast of the output power from an existing PV module, linked to a microconverter, and on the outcome of the neighbor PV modules. Finally, this paper proposes also the analysis of the most common error definitions with new mathematical formulations, by comparing their effectiveness and immediate comprehension, in view of increasing power forecasting accuracy and performing both real-time and offline analysis of PV modules performance and possible faults.

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Systematical Evaluation of Solar Energy Supply Forecasts

Cited by 9 publications

References 19 publications

Short‐term photovoltaic prediction based on CNN‐GRU optimized by improved similar day extraction, decomposition noise reduction and SSA optimization

Short‐term photovoltaic prediction based on CNN‐GRU optimized by improved similar day extraction, decomposition noise reduction and SSA optimization

Modeling System Based on Machine Learning Approaches for Predictive Maintenance Applications

PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast

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