Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

Moon, Jihoon; Kim, Junhong; Kang, Pilsung; Hwang, Eenjun

doi:10.3390/en13040886

Cited by 35 publications

(27 citation statements)

References 52 publications

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“…In 1991, Artificial neural network (ANN) was first proposed by Park DC [15] for load forecasting of power system, and back propagation (BP) algorithm was adopted. Afterwards, due to the optimization by intelligence algorithms, the ANN got improved accuracy, but it relied heavily on the quality of training data [16]. Nowadays it is well known that deep neural network (DNN) has dominated load prediction in recent years.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

et al. 2021

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Short-term load forecasting (STLF) with excellent precision and prominent efficiency plays a significant role in the stable operation of power grid and the improvement of economic benefits. In this paper, a novel model based on data mining and deep learning is proposed. Firstly, the preprocessing of data includes normalization of historical load, and fuzzification of influencing factors (meteorological factors, date types and economy) based on Pearson correlation coefficient (PCC). Secondly, kernel fuzzy c-means (KFCM) modified by particle swarm optimization (PSO-KFCM) algorithm clusters the daily load curve. In the clustering experiments, the within-cluster sum of squared error (SSE) index is presented to determine the number of clusters and the clustering validity has a 31.9% enhancement compared with the traditional FCM algorithm. Thirdly, the cosine similarity establishes the resemblance between the prediction date and each cluster, and the similar cluster is determined according to the principle of maximum similarity. Finally, a multivariate and multi-step hybrid model MMCNN-LSTM based on convolution neural network (CNN) and long short-term memory (LSTM) neural network is proposed to forecast the load in following 24 hours, in which similar cluster data is applied to training set. To demonstrate the effectiveness of proposed integrated technique, the accuracy has been verified in three predictive experiments. The fruitful results indicated that the average mean absolute percent error (MAPE) in the entire test set was only 1.34%, a 3.02% reduction compared to a single LSTM.

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

et al. 2021

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“…We considered all the input variables using each input variable for the prediction time point to construct the MRF and DNN models. Therefore, we used 432 input variables, that is, 18 (number of input variables) × 24 (number of prediction time points), for the multistep-ahead STLF [ 39 ]. The two stacking ensemble models consist of two stages, and the second-stage model used the prediction results of the first stage and demonstrated better forecasting performance than many single ML models and existing forecasting models.…”

Section: Resultsmentioning

confidence: 99%

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Jung

Moon

Park

et al. 2021

Sensors

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Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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“…For instance, when we transform the first week of 2015 into two-dimensional data, week and cycle are 1 and 53, respectively, and week x and week y are sin((360/53) * 1) and cos((360/53) * 1), respectively. Further explanations of this transformation can be found in [24].…”

Section: Data Collection and Preprocessingmentioning

confidence: 98%

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

Moon¹,

Jung²,

Park³

et al. 2021

Computers, Materials &Amp; Continua

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One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country's longterm influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a longterm forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country's surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new longterm forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.

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Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

Cited by 35 publications

References 52 publications

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

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