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 Nature Inspired Based Optimize Combination of Time Series and Machine Learning Model for Predict Energy Consumption of a Residence
Tác giả hoặc Nhóm tác giả: Thuy-Linh Le, Dinh-Nhat Truong, Duc Sy Nguyen
Nơi đăng: AIP Publishing; Số: 2560;Từ->đến trang: (040014) 1-14;Năm: 2023
Lĩnh vực: Kỹ thuật; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Energy consumption has gradually increased greenhouse gas concentrations in the atmosphere and is considered the main source of global warming. Therefore, many studies have been developed prediction models with higher accuracy and lower feature cost by the day. This study proposed a time series forecasting model by integrates a new nature-inspired optimization algorithm (Jellyfish Algorithm) with a machine learning model (least squares support vector regression). Notably, the data set will be pre-processing to build the most efficient model and the parameters of machine learning are optimized by Jelly Fish Algorithm. The proposed model was evaluated by a real- time dataset and was compared with other regression, times series models, and previous studies. Results reveal that this model is an effective model to predict energy consumption.
ABSTRACT
Energy consumption has gradually increased greenhouse gas concentrations in the atmosphere and is considered the main source of global warming. Therefore, many studies have been developed prediction models with higher accuracy and lower feature cost by the day. This study proposed a time series forecasting model by integrates a new nature-inspired optimization algorithm (Jellyfish Algorithm) with a machine learning model (least squares support vector regression). Notably, the data set will be pre-processing to build the most efficient model and the parameters of machine learning are optimized by Jelly Fish Algorithm. The proposed model was evaluated by a real- time dataset and was compared with other regression, times series models, and previous studies. Results reveal that this model is an effective model to predict energy consumption.
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