Mobiusi

2012-2014 Customer Hourly Electricity Consumption Dataset

#time series forecasting #anomaly detection #energy consumption analysis #smart grid #electricity demand forecasting #energy management
  • 500
  • 1.5G
  • CSV
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

With the growth of global energy consumption, the electricity industry faces significant challenges in predicting electricity demand and optimizing supply. Existing solutions often rely on historical electricity consumption patterns, but still have shortcomings in accuracy and real-time response. This dataset aims to address the uncertainty in electricity consumption forecasting and meet the needs of smart grid scheduling and energy management. Data collection is primarily conducted through smart meters in normal residential and commercial environments, ensuring that electricity consumption data at different times is recorded. Quality control includes multiple rounds of data validation and expert review to ensure data accuracy and consistency. The annotation team consists of experts in the fields of electrical engineering and data analysis. Data preprocessing steps include timestamp alignment and missing data interpolation using various technical methods. Data is organized in CSV format. All data has undergone structured processing for easy access and analysis.

Sample Examples

Technical Specifications

FieldTypeDescription
file_namestringFile name
durationstringDuration
qualitystringResolution
customer_idstringUnique identifier for the customer.
consumptionfloatThe amount of electricity consumed by the customer during the specified hour, measured in kilowatt-hours (kWh).
measurement_timedatetimeThe specific timestamp when the power consumption was recorded.
anomaly_flagbooleanIndicates whether the record represents an anomalous consumption pattern.
seasonstringThe season during which the record was measured, such as spring, summer, etc.
weekdaystringThe day of the week corresponding to the record.
temperaturefloatThe ambient temperature at the time of recording, measured in degrees Celsius.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What research fields is this dataset suitable for?
This dataset is suitable for research in energy management, smart grid analysis, and electricity consumption behavior studies.
What trends can be studied using this dataset?
Trends such as seasonal or time-of-day electricity consumption, peak usage periods, and annual consumption changes can be studied using this dataset.
How can this dataset be used for time series analysis?
The dataset can be modeled using time series analysis techniques to predict future electricity consumption patterns.
Is this dataset suitable for training machine learning models?
Yes, this dataset can be used to train machine learning models to predict consumption trends or identify anomalous consumption behaviors.
What time series features does this dataset have?
The dataset includes hourly recorded electricity consumption data, enabling in-depth analysis of temporal evolution features.

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Cite this Work

@dataset{Mobiusi2026,
  title={2012-2014 Customer Hourly Electricity Consumption Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/c998eb11576166fd45dc96a7baa0b43e?cate=7},
  urldate={2026-02-04},
  keywords={electricity consumption dataset, time series electricity forecasting, energy management dataset},
  version={1.0}
}

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