基于深度学习的不同季节光伏发电功率预测数据集

由于光伏发电的间歇性和不稳定性，对准确的功率预测需求日益增加。本研究主要面向短期光伏功率预测需求，旨在提高光伏电站的运行效率和电网稳定性。基于澳大利亚电站采集的时间数据、实际光伏功率数据等观测值，预测未来5min的光伏功率，数据选取了不同季节和不同天气条件下的典型预测日进行分析，总数据量为16.7MB.

Due to the intermittency and instability of photovoltaic (PV) power generation, there is a growing demand for accurate power forecasting. This study focuses on short-term photovoltaic power forecasting, aiming to improve the operational efficiency of PV power stations and the stability of power grids. Based on observational data including time-series data and actual photovoltaic power data collected from Australian power stations, this work forecasts the photovoltaic power output in the next 5 minutes. Typical forecasting days across different seasons and weather conditions are selected from the dataset for analysis, with a total data size of 16.7 MB.

搜集汇总

背景与挑战

背景概述

该数据集为基于深度学习的光伏发电功率预测数据集，旨在通过澳大利亚电站的时间序列和实际功率数据，预测未来5分钟的光伏功率。它涵盖了不同季节和天气条件下的典型日数据，总数据量为16.7MB，适用于短期功率预测研究。