Photovoltaic power station power prediction method and device and related equipment

Photovoltaic power station power prediction method and device and related equipment

The invention discloses a photovoltaic power station power prediction method and device and related equipment. The photovoltaic power station power prediction method comprises the steps of obtaining a weather forecast time sequence of a photovoltaic power station in a first time period, and an irrad...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: JIANG CHUNBI, LI ZHE, ZHAO XIN, ZHANG DONGXIAO, CHEN YUNTIAN
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The invention discloses a photovoltaic power station power prediction method and device and related equipment. The photovoltaic power station power prediction method comprises the steps of obtaining a weather forecast time sequence of a photovoltaic power station in a first time period, and an irradiation intensity actual measurement time sequence and a power actual measurement time sequence of the photovoltaic power station in a second time period; inputting each time sequence into the trained power prediction model to obtain a power prediction value of the first time point; wherein the power prediction model is obtained by taking a weather forecast time sequence in a third time period, an irradiation intensity actual measurement time sequence and a power actual measurement time sequence in a fourth time period as training samples and taking weather actual measurement data and a power actual measurement value at a second time point as sample label training. According to the method, the power prediction model