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

Residential Air Conditioning (AC) load has a huge role to play in Demand Response (DR) Programs as it is one of the power intensive and interruptible load in a home. Due to the variety of ACs types and the different sizes of residences, modelling the power consumption of AC load individually is non-trivial. Here, an adaptive framework based on Regression Trees is proposed to model and forecast the power consumption of different AC units in different environments by taking in just 6 basic variables. The framework consists of an automatic feature selection process, a load prediction module, an indoor temperature forecasting module, and is capped off by a load forecasting module. The effectiveness of the proposed approach is evaluated using data set from an ongoing research project on air-conditioning system control for energy management in a residential test bed in Singapore. Experiments on highly dynamic loads gave a maximum Mean Absolute Percentage Error (MAPE) of 21.35% for 30min ahead forecasting and 27.96% for day ahead forecasting.

An Adaptive Data Driven Approach to Single Unit Residential Air-conditioning Prediction and Forecasting using Regression Trees