Short term electricity load forecasting using a hybrid model

Zhang, Jinliang; Wei, Yi‐Ming; Li, Dezhi; Tan, Zhongfu; Zhou, Jian

doi:10.1016/j.energy.2018.06.012

Cited by 243 publications

(105 citation statements)

References 37 publications

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“…As noted by Wang et al [19], the more system factors that can be considered, the higher the forecasting precision. Electricity data rules and characteristics can be easily obtained using decomposition or combination [20]; for example, Zhang et al [21] applied a decomposition approach to forecast short-term electricity demand, in which the data series were split into two new series and two different models trained to forecast these separately, Li et al [22] proved that a random forest technique based on ensemble empirical mode decomposition was able to improve the forecasting accuracy of daily enterprise electricity consumption, Laouafi et al [1] developed a combination methodology for electricity demand forecasting, for which six individual models were applied to the real-time load data, and the final load estimation obtained by adding each model's forecasting value multiplied by its weight, and Chen et al [23] proposed a generalized model for wind turbine faulty condition detection using a combination prediction approach and information entropy.…”

Section: Literature Reviewmentioning

confidence: 99%

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Yang

et al. 2019

Sustainability

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(1) Background: Electricity consumption data are often made up of complex, unstable series that have different fluctuation characteristics in different industries. However, electricity demand forecasting is a prerequisite for the control and scheduling of power systems. (2) Methods: As most previous research has focused on prediction accuracy rather than stability, this paper developed a decomposition-based combination forecasting model using dynamic adaptive entropy-based weighting for total electricity demand forecasting at the engineering level. (3) Results: To further illustrate the prediction accuracy and stationarity of the proposed method, a comparison analysis using an analysis of variance and an orthogonal approach to solve the least squares equations was conducted using classical individual models, a combination forecasting model, and a decomposition-based combination forecasting model. The proposed method had a very satisfactory overall performance with good verification and validation compared to autoregressive integrated moving average (ARIMA) and artificial neural-networks (ANN). (4) Conclusion: As the proposed method dynamically combines various forecast models and can decompose and adapt to various characteristic data sets, it was found to have an accurate, stable forecast performance. Therefore, it could be broadly applied to forecasting electricity demand and developing electricity generation plans and related energy policies.

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Section: Literature Reviewmentioning

confidence: 99%

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Yang

et al. 2019

Sustainability

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“…According to the predicted time range, the power load forecasts can be divided into long-term, medium-term, short-term, and ultra-short-term forecast [5]. is paper mainly focuses on short-term load forecasting, which predicts future loads from minutes to weeks; accurate STLF can help power system staff to develop reasonable production plans, maintain supply and demand balance, and ensure grid safety while reducing resource waste and electricity costs [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Chun

Hao

et al. 2020

Mathematical Problems in Engineering

100

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Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

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“…The electric load forecasting method can be divided into traditional statistical learning-based methods, machine learning methods, and a combined model as well as hybrid forecasting methods [1]. Traditional statistical learning-based methods are accurate and easy to implement, but when the fluctuation of the electric load is severe, they usually perform poorly.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting

et al. 2020

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As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.

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Short term electricity load forecasting using a hybrid model

Cited by 243 publications

References 37 publications

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting

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