杭州市智慧能源平台光伏发电预测分析数据

通过对光伏发电站的光伏设备、气象仪设备的发电数据、设备状态数据以及气象数据进行采集分析预测，能够有效的帮助公司智慧能源一体化平台提供更加实时和准确的数据支撑。主要应用在以下几个方面：（1）在光伏运维方面，可以优化公司光伏站点的运维管理，提前调整运维策略，确保站点的高效运行，降低不稳定性。（2）在储能方面，通过光伏发电量预测，可以优化发电站储能系统的充放电策略，提高储能设备的利用率和经济效益‌。（3）在购售电方面，现货市场要求提前申报发电量，偏差考核成本高昂的情况下，精准预测可显著提升竞价策略。1、数据采集：从本公司物联网平台获取实际发电量、输出交流功率、输入直流功率、无功有功损耗等光伏设备数据以及温湿度等气象仪设备数据。从公司智慧能源一体化平台获取光伏面板清洗频率数据。
2、数据处理：对采集的数据进行聚合与分类，按照日期、站点、设备等维度进行梳理。其中逆变器效率=(输出交流功率/输入直流电功率)x100%；逆变器损耗率= (无功损耗 + 有功损耗)/(输入直流功率 × 运行时间)×100%；预测准确率=(预测发电量-实际发电量)/实际发电量x100%。
3、数据加工：将逆变器效率、逆变器损耗、面板清洗频率以及温湿度气压等数据作为特征数据，进行Min-Max归一化、KNN缺失值补充等方式进行加工。
4、数据应用：使用公司自行训练和部署的LSTM神经网络模型，即通过MYSQL表和对组件进行归一化、拆分处理，运用XGBoost-回归、 RF-回归，借助机器学习预测和模型评估从而实现对未来时间段光伏发电量PV进行数据预测。

Collecting, analyzing and forecasting power generation data, equipment status data and meteorological data from photovoltaic (PV) equipment and meteorological instruments in PV power stations can effectively provide more real-time and accurate data support for the company's Smart Energy Integrated Platform. It is mainly applied in the following aspects:
1. PV Operation and Maintenance: Optimize the operation and maintenance management of the company's PV stations, adjust operation and maintenance strategies in advance to ensure efficient station operation and reduce operational instability.
2. Energy Storage: Through PV power generation forecasting, optimize the charging and discharging strategies of the energy storage system of the power station, improving the utilization rate and economic benefits of energy storage equipment.
3. Power Purchasing and Selling: When the spot market requires advance declaration of power generation and the deviation assessment cost is high, accurate power generation forecasting can significantly optimize the bidding strategy.

1. Data Collection: Obtain PV equipment data including actual power generation, output AC power, input DC power, reactive and active power loss, as well as meteorological instrument data such as temperature and humidity from the company's IoT platform. Obtain PV panel cleaning frequency data from the company's Smart Energy Integrated Platform.
2. Data Processing: Aggregate and classify the collected data, and organize it according to dimensions such as date, station and equipment. The relevant calculation formulas are as follows:
Inverter efficiency = (Output AC Power / Input DC Power) × 100%;
Inverter loss rate = (Reactive Loss + Active Loss) / (Input DC Power × Operating Duration) × 100%;
Prediction Accuracy = (Forecasted Power Generation - Actual Power Generation) / Actual Power Generation × 100%.
3. Data Feature Engineering: Take inverter efficiency, inverter loss rate, PV panel cleaning frequency, temperature, humidity, air pressure and other data as feature data, and process them via methods such as Min-Max normalization and K-Nearest Neighbors (KNN) missing value imputation.
4. Data Application: Adopt the LSTM neural network model independently trained and deployed by the company. Specifically, based on MySQL tables, perform normalization and splitting processing on the dataset, apply XGBoost Regression and Random Forest (RF) Regression, and utilize machine learning prediction and model evaluation to realize the forecasting of PV power generation in future time periods.

搜集汇总

背景与挑战

背景概述

该数据集为杭州市智慧能源平台光伏发电预测分析数据，包含光伏设备、气象仪设备的发电数据、设备状态数据以及气象数据，每日更新，共576条记录。主要应用于光伏运维、储能和购售电场景，通过LSTM神经网络模型进行发电量预测。