A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

Cho, Jintae; Yoon, Yeunggurl; Son, Young‐Suk; Kim, Hongjoo; Ryu, Ho‐Sung; Jang, Gilsoo

doi:10.3390/en15092987

Cited by 10 publications

(8 citation statements)

References 22 publications

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“…The training and validation sets are partitioned uniformly to prevent information leakage during the learning phase. Through ensemble learning strategies [31], training three distinct models: XGBoost, CATBoost, and LightGBM, can form a robust learner. The final prediction is a weighted average of the results from these combined models:…”

Section: Ensemble Learning Layermentioning

confidence: 99%

Electric Load Forecasting Based on Deep Ensemble Learning

Wang,

Yu,

Wang

et al. 2023

Applied Sciences

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Short-to-medium-term electric load forecasting is crucial for grid planning, transformation, and load scheduling for power supply departments. Various complex and ever-changing factors such as weather, seasons, regional economic structures, and enterprise production cycles exert uncontrollable effects on the electric grid load. While the causal convolutional neural network can significantly enhance long-term sequence prediction, it may suffer from problems such as vanishing gradients and overfitting due to extended time series. To address this issue, this paper introduces a new power load data anomaly detection method, which leverages a convolutional neural network (CNN) to extract temporal and spatial information from the load data. The features extracted are then processed using a bidirectional long short-term memory network (BiLSTM) to capture the temporal dependencies in the data more adeptly. An enhanced random forest (RF) classifier is employed for anomaly detection in electric load data. Furthermore, the paper proposes a new model framework for electricity load forecasting that combines a dilated causal convolutional neural network with ensemble learning. This combination addresses issues such as vanishing gradients encountered in causal convolutional neural networks with long time series. Extreme gradient boosting (XGBoost), category boosting (CATBoost), and light gradient boosting machine (LightGBM) models act as the base learners for ensemble modeling to comprehend deep cross-features, and the prediction results generated by ensemble learning serve as a new feature set for secondary ensemble modeling. The dilated convolutional neural network broadens the receptive field of the convolutional kernel. All acquired feature values are concatenated and input into the dilated causal convolutional neural network for training, achieving short-to-medium-term electric load forecasting. Experimental results indicate that compared to existing models, its root mean squared error (RMSE) and mean squared error (MSE) in short-term and mid-term electricity load forecasting are reduced by 4.96% and 12.31%, respectively, underscoring the efficacy of the proposed framework.

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Section: Ensemble Learning Layermentioning

confidence: 99%

Electric Load Forecasting Based on Deep Ensemble Learning

Wang,

Yu,

Wang

et al. 2023

Applied Sciences

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“…Thus, variables in darker green exhibit a higher linear correlation relationship. In our paper, the criteria for how to use the Pearson correlation coefficients to select important features are the same as those used by Cho et al [29]. That is, a Pearson correlation coefficient of 0.7 or more indicates a strong linear correlation, so variables with a Pearson correlation coefficient of 0.7 or higher are selected.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Because the weekly GDP is affected by various factors, such as holidays, this average distribution method is not an accurate estimate of weekly GDP, which results in increasing the error of load forecasting. In one study [29], the nominal monthly GDP data were estimated from annual data by the curve fitting interpolation method. Although the curve fitted by this interpolation method is consistent with the trend in the annual data, the trend in the monthly data of various years may vary.…”

Section: Data Preparationmentioning

confidence: 99%

Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

Jin

Zeng

et al. 2022

Energies

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Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

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“…It is important to note that LSTM's effectiveness is demonstrated by its victory in the M4 forecasting competition of 2018, which employed 100,000 real-world time series [7]. Thus, the LSTM model and its variations [8][9][10][11][12][13][14][15], as well as their combinations with other forecasting models [16][17][18][19][20][21][22][23], are typically utilized for forecasting medium-term loads, as with other load forecasting timeframes.…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

Yaprakdal,

Varol Arısoy

2023

Applied Sciences

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In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operation. Although statistical methods were primarily used because ELF is a time series problem, deep learning (DL)-based forecasting approaches are more commonly employed and successful in achieving precise predictions. However, these DL-based techniques, known as black box models, lack interpretability. When interpreting the DL model, employing explainable artificial intelligence (XAI) yields significant advantages by extracting meaningful information from the DL model outputs and the causal relationships among various factors. On the contrary, precise load forecasting necessitates employing feature engineering to identify pertinent input features and determine optimal time lags. This research study strives to accomplish a mid-term forecast of ELF study load utilizing aggregated electrical load consumption data, while considering the aforementioned critical aspects. A hybrid framework for feature selection and extraction is proposed for electric load forecasting. Technical term abbreviations are explained upon first use. The feature selection phase employs a combination of filter, Pearson correlation (PC), embedded random forest regressor (RFR) and decision tree regressor (DTR) methods to determine the correlation and significance of each feature. In the feature extraction phase, we utilized a wrapper-based technique called recursive feature elimination cross-validation (RFECV) to eliminate redundant features. Multi-step-ahead time series forecasting is conducted utilizing three distinct long-short term memory (LSTM) models: basic LSTM, bi-directional LSTM (Bi-LSTM) and attention-based LSTM models to accurately predict electrical load consumption thirty days in advance. Through numerous studies, a reduction in forecasting errors of nearly 50% has been attained. Additionally, the local interpretable model-agnostic explanations (LIME) methodology, which is an explainable artificial intelligence (XAI) technique, is utilized for explaining the mid-term ELF model. As far as the authors are aware, XAI has not yet been implemented in mid-term aggregated energy forecasting studies utilizing the ELF method. Quantitative and detailed evaluations have been conducted, with the experimental results indicating that this comprehensive approach is entirely successful in forecasting multivariate mid-term loads.

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A Study on Load Forecasting of Distribution Line Based on Ensemble Learning for Mid- to Long-Term Distribution Planning

Cited by 10 publications

References 22 publications

Electric Load Forecasting Based on Deep Ensemble Learning

Electric Load Forecasting Based on Deep Ensemble Learning

Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load

A Multivariate Time Series Analysis of Electrical Load Forecasting Based on a Hybrid Feature Selection Approach and Explainable Deep Learning

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