A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Li, Lianhui; Mu, Chunyang; Ding, Shaohu; Wang, Zheng; Mo, Runyang; Song, Yongfeng

doi:10.3390/en9010020

Cited by 6 publications

(7 citation statements)

References 38 publications

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“…Grid load forecasting refers to periodic load forecasting for power equipment such as generator sets, power users, and power loads, so as to make reasonable planning to ensure the safe and stable operation of power system. Short-term load forecasting (STLF) is one of the important tasks of energy utilization, power planning, dispatching, and other management services [ 1 – 4 ]. In fact, considering the different operation laws and performance of each power system, on the premise that the transmission accuracy requirements are not discounted, and considering the different influencing factors (including natural environment, human development, social change, etc.…”

Section: Introductionmentioning

confidence: 99%

Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks

Feng

2022

Computational Intelligence and Neuroscience

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Deep learning is good at extracting the required feature quantity from the massive input information through multiple hidden layers and completing the learning through training to achieve the task of load forecasting. The impulse power load data contain a lot of noise, burrs, and strong randomness. As an improved recurrent neural networks, the output of long short-term memory (LSTM) network is not only related to the current input, but also closely related to the historical information, which can effectively predict the impact power load. An impulse power load forecasting model based on improved recurrent neural networks is proposed. To solve the training difficulties caused by deep networks, database is divided into training data set and test data set. To accelerate running speed and improve accuracy and reliability, parameter setting in deep learning neural network is analyzed. The proposed load forecasting model is verified by simulation and compared with the existing methods. Taking the average relative error as the standard, the effectiveness of the proposed model for the forecasting of impulse power load connected to the bus is verified.

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Section: Introductionmentioning

confidence: 99%

Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks

Feng

2022

Computational Intelligence and Neuroscience

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“…Researchers then realized that the accuracy of a prediction result can be improved by giving different weights to different methods. Recently, several studies have assigned different weights to each time point of different methods (i.e., variable-weight combination forecasting) [41][42][43][44]; however, the determination of the weights is a major problem in these studies.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

Wang

Sun

2018

Sustainability

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It is widely considered that solar energy will be one of the most competitive energy sources in the future, and solar energy currently accounts for high percentages of power generation in developed countries. However, its power generation capacity is significantly affected by several factors; therefore, accurate prediction of solar power generation is necessary. This paper proposes a photovoltaic (PV) power generation forecasting method based on ensemble empirical mode decomposition (EEMD) and variable-weight combination forecasting. First, EEMD is applied to decompose PV power data into components that are then combined into three groups: low-frequency, intermediate-frequency, and high-frequency. These three groups of sequences are individually predicted by the variable-weight combination forecasting model and added to obtain the final forecasting result. In addition, the design of the weights for combination forecasting was studied during the forecasting process. The comparison in the case study indicates that in PV power generation forecasting, the prediction results obtained by the individual forecasting and summing of the sequences after the EEMD are better than those from direct prediction. In addition, when the single prediction model is converted to a variable-weight combination forecasting model, the prediction accuracy is further improved by using the optimal weights.

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“…A robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination was proposed in [11]. In particular, the authors proposed an Immune Algorithm-Particle Swarm Optimization that was applied to Chinese data.…”

Section: Related Workmentioning

confidence: 99%

A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series

et al. 2016

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Abstract:The occurrence of outliers in real-world phenomena is quite usual. If these anomalous data are not properly treated, unreliable models can be generated. Many approaches in the literature are focused on a posteriori detection of outliers. However, a new methodology to a priori predict the occurrence of such data is proposed here. Thus, the main goal of this work is to predict the occurrence of outliers in time series, by using, for the first time, imbalanced classification techniques. In this sense, the problem of forecasting outlying data has been transformed into a binary classification problem, in which the positive class represents the occurrence of outliers. Given that the number of outliers is much lower than the number of common values, the resultant classification problem is imbalanced. To create training and test sets, robust statistical methods have been used to detect outliers in both sets. Once the outliers have been detected, the instances of the dataset are labeled accordingly. Namely, if any of the samples composing the next instance are detected as an outlier, the label is set to one. As a study case, the methodology has been tested on electricity demand time series in the Spanish electricity market, in which most of the outliers were properly forecast.

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A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination

Cited by 6 publications

References 38 publications

Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks

Research on Impulse Power Load Forecasting Based on Improved Recurrent Neural Networks

Photovoltaic Power Forecasting Based on EEMD and a Variable-Weight Combination Forecasting Model

A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series

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