Power load forecasting based on grey neural network

Zhang, Dahai; Ren, Zhiwu; Bi, Yanbing; Zhou, Dazhou; Bi, Yanqiu

doi:10.1109/isie.2008.4677085

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…3) Data integration and transformation: Load data may come from multiple data sources,including different databases, data cubes, or general text files, etc. Data integration is to put datas into unique data store [3]. Data integration relate to detecting and treating data conflict.…”

Section: Selection Of Data and Pretreatmentmentioning

confidence: 99%

The Short-Term Load Forecasting Based on Rough Set

Jian

2011

AMR

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Short-term load forecasting in power system is an important daily work in Dispatch Operation Department of Power System. The level of forecasting accuracy directly affects the operating economy and supply quality of power system.This paper adopts the rough sets to forecast short-term load. It designs an overall structure of forecasting the short-term load based on the rough sets, applies the rough sets to analyze the importance of attribute of each condition on decision-making attribute and then gets a reduced forecasting system, lists examples to forecast short-term load on the basis of the real historical data, and compare the results with the traditional decision-making tree algorithm. The results of this study prove that the rough sets is much practical in short-term load forecasting.

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Section: Selection Of Data and Pretreatmentmentioning

confidence: 99%

The Short-Term Load Forecasting Based on Rough Set

Jian

2011

AMR

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“…The graph neural network has also been extensively studied in the field of photovoltaic power generation prediction. For example, the timeseries of photovoltaic power production at multiple sites is modeled as the signal on the graph [48][49][50]. The graph neural network and the long-and short-term memory recurrent neural network were combined into a spatio-temporal GNN model to analyze the temporal and spatial characteristics of the historical data of photovoltaic power stations [26,50,51].…”

Section: Introductionmentioning

confidence: 99%

Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

et al. 2023

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The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.

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“…Gray neural network is suitable for middle and long term load forecasting, and case study shows that its forecasting accuracy is better than that of neural network and grey prediction method [9]. According to the random-increase and non-linearity relationship, the improved ant colony method is used as the basis of combination weight making of gray neural network, so as to achieve the goal of optimizing the whole forecasting precision and find the combination weight that can exhibit the high consistency and high precision for the series values, finally the whole forecasting accuracy can be improved obviously [4].…”

Section: Introductionmentioning

confidence: 99%

Back Propagation Neural Network for Short-term Electricity Load Forecasting with Weather Features

Wang

et al. 2009

2009 International Conference on Computational Intelligence and Natural Computing

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In this paper, we expand previous work and present an accurate electricity load forecasting algorithm with back propagation neural networks. It contributes to short-term electricity load forecast methodology with neural network with weather feature such as max centigrade, min centigrade and weather types. The original electricity load is from shanghai district, which is composed of original every 5 minutes load records. Through the data transform the every hour data average 12 original records and weather feature become double value. After training and simulation, the prediction errors fit for the application needs. The algorithm besides other algorithms has been used in electricity load forecasting software. Many results confirm that the proposed method is capable of forecasting load efficiently.

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Power load forecasting based on grey neural network

Cited by 8 publications

References 9 publications

The Short-Term Load Forecasting Based on Rough Set

The Short-Term Load Forecasting Based on Rough Set

Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

Back Propagation Neural Network for Short-term Electricity Load Forecasting with Weather Features

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