Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction

Wang, Xiaomei; Lai, Chun Sing; Bai, Chenchen; Lai, Loi Lei; Zhang, Qi; Liu, Bo

doi:10.3390/en13143592

Cited by 30 publications

(19 citation statements)

References 55 publications

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“…Extreme learning machine (ELM) is based on the feedforward neural network, which is often used to solve the problems of unsupervised learning and supervised learning [50,51]. e structure of ELM model includes the hidden layer, the output layer, and the input layer.…”

Section: Principle Of Extreme Learning Machinementioning

confidence: 99%

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Kan

Lin

et al. 2021

Computational Intelligence and Neuroscience

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When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.

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Section: Principle Of Extreme Learning Machinementioning

confidence: 99%

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Kan

Lin

et al. 2021

Computational Intelligence and Neuroscience

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“…The installed power capacity of renewable energy generation grew more than 200 GW, which is mostly PV generation in 2019 [4,5]. However, because of the intermittency and uncertainty of PV, the high penetration of PV could bring great challenges to the power grid, such as power distribution system planning and operation [6][7][8][9], load demand forecasting [10][11][12], hybrid energy system configuration [13,14], and PV power forecasting [15,16].…”

Section: Background and Motivationmentioning

confidence: 99%

“…The net load measured by smart meters was used to estimate the individual distributed PV's capacity and generation power in [15] through a support vector regression model. Based on the hybrid data dimension reduction method and mapping function, the authors of [32] provided a data-driven method to predict BTM PV generation power.…”

Section: Literature Reviewmentioning

confidence: 99%

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

et al. 2020

Self Cite

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Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.

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“…This method of screening features is to rely on the sample entropy as a loss function to determine whether the features meet the experimental conditions, which can improve the prediction accuracy to a certain extent. Wu et al [22] combined VMD and FA-KELM to improve the accuracy of forecasting photovoltaic power generation in the day and proposed a new hybrid model of photovoltaic power generation output prediction interval, using VMD to decompose the photovoltaic power sequence, and each decomposed component obtain a prediction result through FA-KELM, and continue to sum the prediction results, through cubic spline interpolation can obtain the confidence interval of predicted photovoltaic power. Experiments show that the mixed-use of VMD and FA-KELM can effectively construct the best confidence interval and obtain a more accurate photovoltaic power output prediction interval.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

Shen

Deng

et al. 2021

Sustainability

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Electricity load forecasting is one of the hot concerns of the current electricity market, and many forecasting models are proposed to satisfy the market participants’ needs. Most of the models have the shortcomings of large computation or low precision. To address this problem, a novel deep learning and data processing ensemble model called SELNet is proposed. We performed an experiment with this model; the experiment consisted of two parts: data processing and load forecasting. In the data processing part, the autocorrelation function (ACF) was used to analyze the raw data on the electricity load and determine the data to be input into the model. The variational mode decomposition (VMD) algorithm was used to decompose the electricity load raw-data into a set of relatively stable modes named intrinsic mode functions (IMFs). According to the time distribution and time lag determined using the ACF, the input of the model was reshaped into a 24 × 7 × 8 matrix M, where 24, 7, and 8 represent 24 h, 7 days, and 8 IMFs, respectively. In the load forecasting part, a two-dimensional convolutional neural network (2D-CNN) was used to extract features from the matrix M. The improved reshaped layer was used to reshape the extracted features according to the time order. A temporal convolutional network was then employed to learn the reshaped time-series features and combined with the fully connected layer to complete the prediction. Finally, the performance of the model was verified in the Eastern Electricity Market of Texas. To demonstrate the effectiveness of the proposed model data processing and load forecasting, we compared it with the gated recurrent unit (GRU), TCN, VMD-TCN, and VMD-CNN models. The TCN exhibited better performance than the GRU in load forecasting. The mean absolute percentage error (MAPE) of the TCN, which was over 5%, was less than that of the GRU. Following the addition of VMD to the TCN, the basic performance of the model was 2–3%. A comparison between the SELNet model and the VMD-TCN model indicated that the application of a 2D-CNN improves the forecast performance, with only a few samples having an MAPE of over 4%. The model’s prediction effect in each season is discussed, and it was found that the proposed model can achieve high-precision prediction in each season.

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Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction

Cited by 30 publications

References 55 publications

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting

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