Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation

Gu, Bing-Lin; Zhang, Tianren; Meng, Hang; Zhang, Jinhua

doi:10.1016/j.renene.2020.09.087

Cited by 104 publications

(33 citation statements)

References 25 publications

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“…Then, we calculated the distance of the object to be evaluated from the positive and auxiliary negative ideal solutions according to Equations (18)- (19). Finally, the relative closeness of each province was calculated by Equation (20). The results are shown in Table 5.…”

Section: Comprehensive Evaluation Resultsmentioning

confidence: 99%

“…Among them, the entropy weight method has the advantage of distinguishing between indicators. Subjective weighting methods include the analytic hierarchy process (AHP) [18], the Delphi method [19], the cloud model [20], etc. Compared with other subjective weighting models, the cloud model has the advantage of uncertainty transformation, which can maximize the retention of the inherent uncertainty in the evaluation process and improve the credibility of the evaluation results.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Provincial Carbon Neutrality Capacity of China Based on Combined Weight and Improved TOPSIS Model

Niu

et al. 2021

Sustainability

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It will be a huge challenge for China to achieve carbon neutrality by 2060. At present, China needs to understand its own carbon neutrality status and then scientifically plan a path to achieve carbon neutrality. In order to evaluate the carbon neutrality capacity of China’s provinces, this paper firstly constructs an evaluation indicator system, which includes 20 indicators at six levels. Then, a combination of subjective and objective weighting methods, as well as an improved technique for order preference by similarity to an ideal solution (TOPSIS) model, are used to calculate evaluation results. On this basis, the reasons for their different carbon neutrality capacities are analyzed. The results show that the use of renewable energy, maintaining ecological environmental quality, and low-carbon technology are important factors affecting China’s carbon neutrality capacity, and according to the evaluation results, China’s provinces are divided into three categories. Finally, corresponding suggestions for speeding up the pace of carbon neutrality are put forward.

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Section: Comprehensive Evaluation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Provincial Carbon Neutrality Capacity of China Based on Combined Weight and Improved TOPSIS Model

Niu

et al. 2021

Sustainability

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“…Besides traditional AI/ML models, deep learning and extreme learning machines are also commonly applied in wind speed forecasting. Notable architectures include Kernel Extreme Learning Machine (KELM) [13,14], Long Short-Term Memory (LSTM) [15,16], Echo State Network [17], Deep Belief Network (DBN) [18,19], and Convolutional Neural Network (CNN) [20,21].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

2021

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Markov chains (MC) are statistical models used to predict very short to short-term wind speed accurately. Such models are generally trained with a single moving window. However, wind speed time series do not possess an equal length of behavior for all horizons. Therefore, a single moving window can provide reasonable estimates but is not an optimal choice. In this study, a forecasting model is proposed that integrates MCs with an adjusting dynamic moving window. The model selects the optimal size of the window based on a similar approach to the leave-one-out method. The traditional model is further optimized by introducing a self-adaptive state categorization algorithm. Instead of synthetically generating time series, the modified model directly predicts one-step ahead wind speed. Initial results indicate that adjusting the moving window MC prediction model improved the forecasting performance of a single moving window approach by 50%. Based on preliminary findings, a novel hybrid model is proposed integrating maximal overlap discrete wavelet transform (MODWT) with auto-regressive integrated moving average (ARIMA) and adjusting moving window MC. It is evident from the literature that MC models are suitable for predicting residual sequences. However, MCs were not considered as a primary forecasting model for the decomposition-based hybrid approach in any wind forecasting studies. The improvement of the novel model is, on average, 55% for single deep learning models and 30% for decomposition-based hybrid models.

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“…Thus, the prediction accuracy is improved. At present, deep learning technology has been used in load forecasting [35]- [37], wind power output forecasting [38]- [40], and other fields. It has achieved good prediction results.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

Zhang

Zhu

et al. 2021

IEEE Access

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Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed to optimize a deep belief network (DBN). Fast correlation-based filter (FCBF) method is used to select a meteorological feature set with the best correlation with PV output and avoid redundancy among meteorological factors affecting PV output. In addition, a comprehensive similarity index combining European distance and gray correlation degree is proposed to select the similar day. Then, SOA is used to optimize the number of neurons and the learning rate parameters in DBN. Based on the nonuniform mutation and opposition-based learning method, an improved seagull optimization algorithm (ISOA) with higher optimization accuracy is proposed. Finally, the ISOA-DBN prediction model is established, and the experimental analysis is conducted using the actual data of PV power stations in Australia. Results show that compared with DBN, support vector machine (SVM), extreme learning machine (ELM), radial basis function (RBF), Elman, and back propagation (BP), the mean absolute percentage error indicator of ISOA-DBN is only 1.512% on a sunny day, 5.975 on a rainy day, 3.359 on a cloudy to sunny day, and 1.911% on a sunny to cloudy day. Therefore, the good accuracy of the proposed model is verified. INDEX TERMSPV power generation, prediction model, similar day, seagull optimization algorithm, deep belief network NOMENCLATURE PV photovoltaic SOA seagull optimization algorithm DBN deep belief network FCBF fast correlation-based filter ISOA improved seagull optimization algorithm SVM support vector machine ELM extreme learning machine RBF radial basis function BP back propagation CEEMD complementary ensemble empirical mode decomposition SU symmetric uncertainty GR global radiation DR diffused radiation RBM restricted Boltzmann machines GBRBM Gauss-Bernoulli-restricted Boltzmann machine PSO particle swarm optimization GA genetic algorithm GOA grasshopper optimization algorithm RMSE root mean square error MAPE mean absolute percent error

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Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation

Cited by 104 publications

References 25 publications

Evaluation of Provincial Carbon Neutrality Capacity of China Based on Combined Weight and Improved TOPSIS Model

Evaluation of Provincial Carbon Neutrality Capacity of China Based on Combined Weight and Improved TOPSIS Model

Short-Term Wind Speed Forecasting Based on Hybrid MODWT-ARIMA-Markov Model

Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

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