Lork, ClementClementLorkZhou, YurenYurenZhouBatchu, RajasekharRajasekharBatchuYuen, ChauChauYuenPindoriya, Naran M.Naran M.Pindoriya2025-08-302025-08-302017-01-01[9789897582417]10.5220/00063095006700762-s2.0-85025450085https://d8.irins.org/handle/IITG2025/22576Residential 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.trueData driven | Feature selection | Forecasting | Machine learning | Regression trees | Residential AC modellingConference Paperhttps://doi.org/10.5220/000630950067007667-7620175cpConference Proceeding4