园区夜间能耗异常检测分析数据

该能耗异常检测分析数据在为持续的、大规模的能耗数据监控体系提供一个自动化的“哨兵”功能。该模型可针对业务低活跃期或无人值守时段的能耗脉搏进行监测，定位那些偏离正常模式的异常点。通过多维度算法的融合评判，将原本依赖人工经验的、被动的异常排查，转变为一个主动发现、早期预警的智能化流程。这不仅提升了运营管理的效率和可靠性，更能通过对异常根源的追溯，为优化决策提供数据洞察，从而实现成本的节约与系统稳健性的增强。1.数据来源：收集公司研发的智慧能源软件产品在浙江地区2023年1日1月到2024年12月31日某园区夜间能耗数据，包括年份、日期、夜间总耗电（kWh）、夜间常用设备总能耗（kWh）、夜间常开设备能耗比例等字段，其中“夜间常开设备能耗比例”=“夜间总耗电”/“夜间常用设备总能耗”；2.算法加工：能耗数据的异常检测分析采用了多算法融合的策略，通过Z分数法计算每个数据点相对于整体分布的标准化偏差（公式：Z = |X-μ|/σ），当Z值超过3倍标准差时标记为异常；运用四分位距法基于数据分布的四分位数（Q1、Q3）和四分位距（IQR=Q3-Q1）设定正常值范围（下限：Q1-1.5×IQR，上限：Q3+1.5×IQR），超出该范围的数据被视为异常；针对能耗比例逻辑设置了多重校验规则，包括比例超过150%、比例为负值、零能耗时比例非零、设备能耗大于总能耗等异常情形；最后采用孤立森林算法（参数包含树数量、采样策略、污染率、特征数、随机种子）进行无监督异常检测，得到孤立森林异常分数；3.最终评估：综合各算法结果通过加权平均（公式：综合异常评分 = (Z分数异常 + 四分位距异常 + 比例逻辑异常 + 孤立森林异常) / 4）生成0-1的异常评分，并依据评分区间划分为三个等级：评分≤0.3为正常，0.3<评分≤0.7为可疑，评分>0.7为异常。

This energy consumption anomaly detection and analysis dataset provides an automated 'sentinel' function for continuous, large-scale energy consumption data monitoring systems. This model can monitor energy consumption trends during periods of low business activity or unattended hours, and identify anomalies that deviate from normal patterns. Through the fusion and evaluation of multi-dimensional algorithms, it transforms the previously manual experience-dependent, passive anomaly investigation into an intelligent workflow for proactive discovery and early warning. This not only improves the efficiency and reliability of operation management, but also provides data insights for optimized decision-making by tracing the root causes of anomalies, thereby achieving cost savings and enhanced system robustness.
1. Data Source: Collected nighttime energy consumption data of a certain park in Zhejiang Province from January 1, 2023 to December 31, 2024 from the smart energy software products developed by the company. The data includes fields such as year, date, total nighttime electricity consumption (kWh), total energy consumption of commonly used nighttime equipment (kWh), and energy consumption proportion of always-on nighttime equipment. The formula for 'energy consumption proportion of always-on nighttime equipment' is: 'energy consumption proportion of always-on nighttime equipment' = 'total nighttime electricity consumption' / 'total energy consumption of commonly used nighttime equipment';
2. Algorithm Processing: The anomaly detection and analysis of energy consumption data adopts a multi-algorithm fusion strategy. First, the Z-score method is used to calculate the standardized deviation of each data point relative to the overall distribution (formula: Z = |X-μ|/σ). Data points with a Z-value exceeding 3 times the standard deviation are marked as anomalies. Then, the interquartile range (IQR) method is applied to set the normal value range based on the quartiles (Q1, Q3) of the data distribution and the interquartile range (IQR=Q3-Q1) (lower limit: Q1-1.5×IQR, upper limit: Q3+1.5×IQR). Data exceeding this range are considered anomalies. Multiple verification rules are set for the energy consumption proportion logic, including abnormal scenarios such as proportion exceeding 150%, negative proportion, non-zero proportion when energy consumption is zero, and equipment energy consumption greater than total energy consumption. Finally, the Isolation Forest algorithm (parameters including number of trees, sampling strategy, contamination rate, number of features, random seed) is used for unsupervised anomaly detection to obtain the Isolation Forest anomaly score;
3. Final Evaluation: The comprehensive anomaly score ranging from 0 to 1 is generated by weighted averaging (formula: Comprehensive Anomaly Score = (Z-score Anomaly + IQR Anomaly + Proportion Logic Anomaly + Isolation Forest Anomaly) / 4). Three levels are classified based on the score interval: normal when score ≤ 0.3, suspicious when 0.3 < score ≤ 0.7, and abnormal when score > 0.7.

搜集汇总

背景与挑战

背景概述

该数据集包含700条园区夜间能耗记录，用于异常检测分析，数据格式为xlsx，更新频次按需。它通过多算法融合（如Z分数法、四分位距法和孤立森林算法）自动监测夜间能耗异常，提供综合异常评分和等级划分，旨在实现能耗管理的智能化预警和成本优化。