Short-Term Power Forecasting of Small-Scale Hydropower Based on Projection Pursuit Algorithm

Compared with other traditional energy, the small-scale hydropower which is intermittent energy can not be stored and scheduled. The greater fluctuant of the output power of small-scale hydropower leads to great difficult to the operation of the power system. Most of the existing small-scale hydropower forecasting is considered as the load forecasting factors, and there is not effective forecasting method. This paper establishes an output power forecasting model of the small-scale hydropower based on Projection Pursuit. The simulation results show that the new algorithm has a strong practical application in the small-scale hydropower output power forecasting and the forecast accuracy meets the scheduling requirements.

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Short-term Power Forecasting Method and Application of Wind Farm

MATEC Web of Conferences ◽

10.1051/matecconf/201823204001 ◽

2018 ◽

Vol 232 ◽

pp. 04001

Author(s):

Xiaohu Yang ◽

Rong Ju ◽

Zhe Yuan ◽

Zhenya Zhang

Keyword(s):

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Normal Operation ◽

Prediction Methods ◽

Practical Application ◽

Short Term ◽

Forecasting Method ◽

Wind Power System ◽

Power Forecasting

The prediction of output power of wind farm has important value and significance to the normal operation of some large-scale wind power system. In this paper, the related prediction methods and practical application are studied, and the short-term power forecasting method of the wind power of the vector machine-Markov chain is proposed.

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A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

Energies ◽

10.3390/en12244815 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4815 ◽

Cited By ~ 2

Author(s):

Yosui Miyazaki ◽

Yusuke Kameda ◽

Junji Kondoh

Keyword(s):

Power Systems ◽

Output Power ◽

Power Output ◽

Percentage Error ◽

Support Vector ◽

Short Term ◽

Pv System ◽

Forecasting Method ◽

Geographically Distributed ◽

Power Forecasting

The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.

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Short-term power-forecasting method of distributed PV power system for consideration of its effects on load forecasting

The Journal of Engineering ◽

10.1049/joe.2017.0453 ◽

2017 ◽

Vol 2017 (13) ◽

pp. 865-869 ◽

Cited By ~ 3

Author(s):

Yu Liu ◽

Zhi Li ◽

Kai Bai ◽

Zhaoguang Zhang ◽

Xining Lu ◽

...

Keyword(s):

Power System ◽

Load Forecasting ◽

Short Term ◽

Forecasting Method ◽

Power Forecasting

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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