A short-term prediction model to forecast power of photovoltaic based on MFA-Elman

Ma, Xinyu; Zhang, Xiaohong

doi:10.1016/j.egyr.2022.01.213

Cited by 33 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Elman neural network is a recurrent neural network with local memory units and local feedback connections, which has more capability to deal with dynamically changing data [17], wherein the undertake layer is a specific hidden layer, which receives feedback signals from the hidden layer and then passes forward to the hidden layer through the output of neurons in this layer, completing the local feedback connection and equipping the network with a memory so that the system has the ability to make predictions on time series data, the structure of Elman neural network is shown in Fig. 3.…”

Section: Extraction Of Featuresmentioning

confidence: 99%

Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

Liu¹,

Yan²,

Ma³

et al. 2023

Energy Engineering

View full text Add to dashboard Cite

An operating condition recognition approach of wind turbine spindle is proposed based on supervisory control and data acquisition (SCADA) normal data drive. Firstly, the SCADA raw data of wind turbine under full working conditions are cleaned and feature extracted. Then the spindle speed is employed as the output parameter, and the single and combined normal behavior model of the wind turbine spindle is constructed sequentially with the preprocessed data, with the evaluation indexes selected as the optimal model. Finally, calculating the spindle operation status index according to the sliding window principle, ascertaining the threshold value for identifying the abnormal spindle operation status by the hypothesis of small probability event, analyzing the 2.5 MW wind turbine SCADA data from a domestic wind field as a sample, The results show that the fault warning time of the early warning model is 5.7 h ahead of the actual fault occurrence time, as well as the identification and early warning of abnormal wind turbine spindle operation without abnormal data or a priori knowledge of related faults.

show abstract

Section: Extraction Of Featuresmentioning

confidence: 99%

Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

Liu¹,

Yan²,

Ma³

et al. 2023

Energy Engineering

View full text Add to dashboard Cite

show abstract

“…• Occurrences of global minima and stagnation issues [3][4][5][6][7] • Scalability problems on the normalization procedures adopted [2,8,[12][13][14][15][16][17] • Over-fitting and under-fitting issues [5, 6, 9-11, 23, 48, 51] • Dimensionality constraints of the solar farm data and data handling issues [18][19][20][21][22][23][24] • Elapsed training time [29,31,37] • Data extraction problems in regression based ML models [10][11][12][13][14][15] • Higher number of trainable parameters in DL models [1, 14, 19-20, 26, 27, 43, 47] • Repetitive training of deep neural networks [19,20,26,27] • High computational overhead due to repetitive process [29][30][31][32][33][34][35][36] • Few predictor models with high complexity and data redundancy [45][46][47]…”

Section: Challengesmentioning

confidence: 99%

“…Table 1 details the installed megawatt capacity of solar farms and their percentage contribution of solar power across the globe. The need and importance of power generation from solar source is well lucid considering the abundance sun natural source and difficulty in handling of other forms of energy production [1][2][3]. Due to which, each and every country takes immense steps in building high potential solar farms and thereby to increase the rate of renewable source of power production from their country.…”

Section: Introductionmentioning

confidence: 99%

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

View full text Add to dashboard Cite

A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

show abstract

“…Another strategy to enhance the quality of the training sample is the formation of a meteorological matrix, which describes the day's weather conditions. Further analysis of the basic weather types, such as sunny, cloudy, and rainy, is carried out using readily available meteorological data [14], [15], [16]. To find a method that can obtain appropriate training samples under various complicated weather conditions, indicators such as cosine similarity [17], Euclidean distance [18], and gray correlation [19] are often adopted to evaluate the similarity among samples.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Method for Ultra-Short-Term PV Power Forecasting Based on Data-Driven

et al. 2023

View full text Add to dashboard Cite

To promote the real-time dispatching of a power grid and balanced decision-making of power producers, accuracy and real-time forecasting are two main problems that need to be solved in ultra-shortterm photovoltaic (PV) forecasting. Focusing on the problems of slow model training speed and low forecasting accuracy due to the redundancy of training data samples and insufficient long periodic capture of data in complex weather, this paper proposes a two-stage method for ultra-short-term PV power forecasting based on data-driven. In the meteorological analysis stage, the generation power samples similar to the forecast day were extracted by inputting daily meteorological features and using maximal information coefficient (MIC) weighted grey correlation degree to form the corresponding forecast data set. In the power forecasting stage, the temporal convolutional used network (TCN) extracts local features to maintain the sequence of extracted features. Then the bidirectional gating unit (BiGRU) combined with the Skip connection strategy was used to fully learn the long and the short time sequence of photovoltaic sequences, and the attention mechanism was used to pay adaptive attention to the more important historical states. This study experimented on the measured data from a photovoltaic power station in Southeastern China. The experimental results show that this method is effective in photovoltaic power short-term forecasts. In addition, compared with some traditional forecasting models, this method has smaller forecasting errors and higher robustness in the time scales of 15 minutes, 30 minutes, 45 minutes, and 60 minutes.

show abstract

A short-term prediction model to forecast power of photovoltaic based on MFA-Elman

Cited by 33 publications

References 17 publications

Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

Wind Turbine Spindle Operating State Recognition and Early Warning Driven by SCADA Data

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

A Two-Stage Method for Ultra-Short-Term PV Power Forecasting Based on Data-Driven

Contact Info

Product

Resources

About