电厂负荷调度系统预测数据

为了确保电力供需平衡，避免供电不足或供电过剩的情况发生，利用负荷调度系统的历史用电数据信息，结合预测模型，对未来电力负荷进行精准预测。基于这些预测，调控员可以科学规划发电计划，平衡电力供需，避免资源浪费。首先进行数据采集，通过负荷系统采集到一月之中每天的负荷量以及平均气温和是否节假日的数据集；接着通过简单移动平均法预测未来一个月的负荷趋势。将过去若干个时间点的负荷数据求平均，然后将平均值作为未来时间点的负荷预测值。
计算方式：Lp = (L1 + L2 + ... + Ln) / n，其中Lp表示预测值，L表示负荷量，n表示移动步数。
具体算法过程：以2023年5月的测算数据为例，为了利用简单移动平均法预测2023年6月每天的负荷量，首先确定移动平均的窗口大小（即过去多少天的数据用于计算平均值）。这里为了简化计算并展示方法，选择一个较小的窗口大小，5天。接下来，使用2023年5月最后5天的数据（即5月27日至5月31日）来计算6月1日的预测负荷量。然后，随着新数据的加入（即实际6月1日的负荷量，但在这里假设进行的是前瞻性预测，所以不使用实际数据），通过不断移动窗口来预测6月接下来的每一天，最终得到2023年6月的负荷量预测值。

To ensure the balance between power supply and demand, and avoid both insufficient and excessive power supply, historical power consumption data from the load dispatching system is combined with forecasting models to accurately predict future power loads. Based on these predictions, power dispatchers can scientifically plan power generation schedules, balance power supply and demand, and prevent resource waste.

First, data collection is conducted: the dataset includes daily load, average temperature, and holiday status for each day within a month, collected via the load dispatching system.

Next, the simple moving average method is adopted to forecast the load trend of the upcoming month. The core logic of this method is to calculate the average of load data from several past time points, and take this average as the predicted load value for future time points.

The calculation formula is: Lp = (L1 + L2 + ... + Ln) / n, where Lp represents the predicted value, L denotes the load quantity, and n represents the moving window size.

Take the measured data of May 2023 as an example to demonstrate the simple moving average method for predicting daily load in June 2023. First, determine the window size of the moving average, i.e., the number of past days' data used for calculating the average. To simplify the calculation and illustrate the method, a small window size of 5 days is selected here. Subsequently, the predicted load for June 1 is calculated using the data from the last 5 days of May 2023 (i.e., May 27 to May 31). Then, as new data would be added (for this forward-looking prediction, actual observed data from June is not used), the window is continuously shifted to predict each subsequent day in June. Finally, the predicted load values for all days in June 2023 are obtained.

搜集汇总

数据集介绍

特点

该数据集包含电厂负荷调度系统的预测数据，主要用于电力供需平衡的预测和规划。数据规模为661条，每月更新，采用简单移动平均法进行预测。