English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The energy consumption associated with non-domestic buildings represents 11% of the UK’s total energy consumption, 11% of Europe and 18% of the USA’s. A nnual non-domestic building energy consumption is often presented in the form of average benchmarks, such as 450kWh/m2/year for a large airconditioned building and 200kWh/m2/year for a small naturally ventilated office. Benchmark values give very little insight into how and where a building consumes energy. While some benchmarks provide a breakdown of energy use by energy category (lights, IT, cooling, heating), these data still fails to demonstrate how the energy associated with each category varies throughout the year. To further understand building energy use, a more detailed data breakdown and analysis is required. The electricity demand data for a variety of school buildings (secondary, primary, specialised) in Scotland has been made available for analysis. This consists of half hourly resolution data spanning several years for 50 s chools, allowing key trends and patterns in energy use to be identified. T hese trends can include differences between annual profiles, differences between winter and summer months, and differences in weekday and weekend energy use. Additionally, the effect of other variables such as climate, user behaviour and general building data on the buildings energy consumption can be investigated. A database of half-hourly school energy demand data, with corresponding building details has been set up and a preliminary analysis preformed. Alternative method of pattern recognition in non-domestic energy usage are discussed, and the variables necessary to calibrate this information. This demonstrates the possibility of creating generic energy profiles and hence new benchmarks.

Energy Consumption In Non-Domestic Buildings: A Review of Schools