A Data Mining Approach Combining $K$ -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting

Wu, Wei; Peng, Mugen

doi:10.1109/jiot.2017.2677578

Cited by 132 publications

(49 citation statements)

References 24 publications

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“…In this section, the literature on wind power forecasting [1,9,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], DSM [38][39][40] and electricity load and price forecasting [41,42] is reviewed. The brief salient features of related literature are presented in Table 1.…”

Section: Related Workmentioning

confidence: 99%

“…To mitigate this risk, wind power forecasting is the most popular method. The wind power is forecasted using classical, statistical, data mining [9,[20][21][22][23][24][25][26][27][28] and artificial intelligence methods [1,[29][30][31][32][33][34]. The accuracy of wind power forecasting is important to avoid demand-supply imbalance.…”

Section: Related Workmentioning

confidence: 99%

“…), hour, day of the year, etc. are used to predict wind power generation [1,9,[26][27][28][29][30][31][32][33][34]37].…”

Section: Related Workmentioning

confidence: 99%

“…The Artificial Neural Networks (ANNs) have widely used for modeling the highly fluctuating wind power data [1,[29][30][31][32]. In [29], the authors forecasted wind power using ensemble ANN.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

et al. 2019

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Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…), hour, day of the year, etc. are used to predict wind power generation [1,9,[26][27][28][29][30][31][32][33][34]37].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

et al. 2019

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“…These models include Ridgelet ANN [7], bagging neural network [8], and Multilayer Perceptron Networks [9].…”

Section: Introductionmentioning

confidence: 99%

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun

Zhang

2018

Mathematical Problems in Engineering

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A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.

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HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows

Zhang

Wang

Zhong

et al. 2022

Int J of Intelligent Sys

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We present HydroFlow, a novel deep generative model for predicting the electricity generation demand of large-scale hydropower stations. HydroFlow uses a latent stochastic recurrent neural network to capture the dependencies in the multivariate time series. It not only utilizes the hidden state of the neural network, but also considers the uncertainty of variables related to natural and social factors. We also introduce an endto-end approach based on generative flows to approximate the posterior distribution of time series with exact likelihoods. Our model is powerful as adding stochasticity to different factors (e.g., reservoir capacity and water-flow measurements) and thus overcomes the expressiveness limitations of deterministic prediction methods. It also enables trainable latent transformations that can improve the model interpretability.We evaluate HydroFlow on the data collected from the hydropower stations of a large-scale hydropower development company. Experimental results show that our model significantly outperforms the state-of-the-art baseline methods while providing explainable results.

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A Data Mining Approach Combining $K$ -Means Clustering With Bagging Neural Network for Short-Term Wind Power Forecasting

Cited by 132 publications

References 24 publications

Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows

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