Short-term Electric Load Prediction Using Multiple Linear Regression Method

Kim, Jun Tae; Cho, Seokheon; Ko, Kabseok; Rao, Ramesh R.

doi:10.1109/smartgridcomm.2018.8587489

Cited by 17 publications

(5 citation statements)

References 10 publications

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“…The existing load forecasting research is mainly divided into statistics-based and learning-based methods, and the latter is the current mainstream method. Statistical methods mainly include multiple linear regression, autoregression, autoregressive moving average, and so on (Kim et al, 2018;Ahmad and Chen, 2019), but they can hardly deal with load data with random and dynamic development. (Yang et al, 2019).…”

Section: Open Access Edited Bymentioning

confidence: 99%

Deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning

Han¹,

Wang³

et al. 2023

Front. Energy Res.

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The rapid growth of distributed generation (DG) and load has highlighted the necessity of optimizing their ways of integration, as their siting and sizing significantly impact distribution networks. However, little attention has been paid to the siting and sizing of new substations which are to be installed. This paper proposes deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning. First, as the model depends on an accurate forecast, Long Short-Term Memory (LSTM) deep neural network is used to forecast DG output and load, where electricity growth rate, bidding capacity of the electric expansion, and industrial difference are all considered. Then, a two-stage stochastic mixed integer bilinear programming model was established for joint DG-substation siting and sizing under uncertainties, where multiple objective functions are comprehensively addressed. By using the Fortuny-Amat McCarl Linearization, the resultant bilinear model is equivalently transformed into a mixed integer linear program, which can be efficiently solved. Finally, stochastic power flow calculation in the IEEE 69-node system is conducted to analyze the influence of electric expansion and DG integration on the node voltage and power flow distribution of the power system. The effectiveness of the proposed method is also verified by simulation tests.

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Section: Open Access Edited Bymentioning

confidence: 99%

Deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning

Han¹,

Wang³

et al. 2023

Front. Energy Res.

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show abstract

“…Traditional forecasting has been thoroughly investigated and widely applied because of its high computational speed, robustness, and ease of implementation [10]. Machine learning methods and their expansions have been the subject of research interest for several decades; they include linear regression [11,12], multiple linear regression [13,14], and KNN [15]. For applications based on linear forecasting, these models are an excellent choice as they reflect the relationships among the features of output load and relevant factors.…”

Section: Related Workmentioning

confidence: 99%

Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System

Nguyen

et al. 2021

Electronics

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With the wide use of the Internet of Things and artificial intelligence, energy management systems play an increasingly important role in the management and control of energy consumption in modern buildings. Load forecasting for building energy management systems is one of the most challenging forecasting tasks as it requires high accuracy and stable operating conditions. In this study, we propose a novel multi-behavior with bottleneck features long short-term memory (LSTM) model that combines the predictive behavior of long-term, short-term, and weekly feature models by using the bottleneck feature technique for building energy management systems. The proposed model, along with the unique scheme, provides predictions with the accuracy of long-term memory, adapts to unexpected and unpatternizable intrinsic temporal factors through the short-term memory, and remains stable because of the weekly features of input data. To verify the accuracy and stability of the proposed model, we present and analyze several learning models and metrics for evaluation. Corresponding experiments are conducted and detailed information on data preparation and model training are provided. Relative to single-model LSTM, the proposed model achieves improved performance and displays an excellent capability to respond to unexpected situations in building energy management systems.

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“…Traditional machine learning methods usually include LR, decision tree (DT), support vector machine (SVM), naive bayes (NB) and Random Forest (RF). These methods are usually based on labeled training data and make predictions by learning data patterns and regularities.LR is usually used for non-stationary data prediction by first clustering the training set data to improve its prediction [8]. The support vector regression (SVR) prediction method can better capture the nonlinear modes hidden in the original data and is widely used in the prediction of nonstationary spares.…”

Section: Introductionmentioning

confidence: 99%

Maintenance Spare Parts Prediction Based on Multilevel Migration Learning CNN-ISE-Attention-BiLSTM

Cui,

Jia,

Gao

et al. 2024

IEEE Access

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Scientific and accurate prediction of the consumption of equipment maintenance spare parts is crucial to improve the efficiency of equipment failure disposal and enhance the competitiveness of enterprises. However, the consumption of some maintenance spare parts is usually affected by a variety of factors, with strong randomness and uncertainty, and it is difficult to accurately predict them. In this paper, a multi-level migration learning CNN-ISE-Attention-BiLSTM prediction model for maintenance spare parts is designed through the study of neural networks, time series and attention mechanisms. Through several sets of experiments, it is proved that the model can better solve the problems of insufficient mining of key information due to insufficient data samples of maintenance spare parts consumption and insufficient mining of spare parts consumption law by ordinary neural networks, and better achieve the prediction of non-stationary maintenance spare parts, which is of great significance for the implementation of the scientific management of maintenance spare parts in enterprises.

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Short-term Electric Load Prediction Using Multiple Linear Regression Method

Cited by 17 publications

References 10 publications

Deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning

Deep learning-aided joint DG-substation siting and sizing in distribution network stochastic expansion planning

Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System

Maintenance Spare Parts Prediction Based on Multilevel Migration Learning CNN-ISE-Attention-BiLSTM

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