# COFACTOR-residential: Hourly electricity and heating data from residential buildings in Norway

## TL;DR

This paper presents three datasets containing hourly electricity and heating data from residential buildings in Norway, useful for energy analysis and modeling.

## Contribution

The novelty lies in providing detailed, structured energy data from various residential building types in Norway, collected through collaboration with operators and users.

## Key findings

- The datasets include energy use data from apartment blocks, cabins, and single-family houses with detailed building information.
- Data is organized into human-readable files with time series durations ranging from months to years.
- The data supports a range of energy-related tasks like forecasting, load profiling, and energy system analysis.

## Abstract

This data descriptor describes three datasets with hourly residential energy use data and building information data from apartments, cabins and single-family houses. The first dataset contains 29 files with energy use data from 29 apartment block condominiums, with hourly time series of both electricity and delivered heat. The second dataset includes 20 files from Risvollan borettslag in Trondheim, covering heating and electricity use at the level of entire apartment blocks and heating centrals, as well as sub-metered electricity data from 1058 individual apartments. The third dataset consists of 194 files with electricity smart meter data from single-family houses, cabins, and apartments equipped with Sikom home energy management systems. The energy use data have been collected in co-operation with distribution system operators and utility providers, directly from building owners as well as from Sikom with permission from their users. Information about the Sikom users have been collected through a user survey. Each dataset is organized into human readable comma-separated txt files with a common structure that includes building information data, hourly records of electricity and heating energy use, and weather variables, with time series durations from several months up to multiple years per building. The data are suitable for tasks such as residential building energy analysis, load profile generation, energy disaggregation, classification tasks, forecasting of energy use, demand flexibility analysis, energy system analysis and other modelling tasks.