Analyzing and Forecasting Laboratory Energy Consumption Patterns Using Autoregressive Integrated Moving Average Models DOI Open Access
Yitong Niu,

Xiongjie Jia,

Chee Keong Lee

et al.

Laboratories, Journal Year: 2024, Volume and Issue: 2(1), P. 2 - 2

Published: Dec. 30, 2024

This study applied ARIMA modeling to analyze the energy consumption patterns of laboratory equipment over one month, focusing on enhancing management in laboratory. By explicitly examining AC and DC equipment, this obtained detailed daily operating cycles periods inactivity. Advanced differencing diagnostic checks were used verify model accuracy white noise characteristics through enhanced Dickey–Fuller testing residual analysis. The results demonstrate model’s predicting consumption, providing valuable insights into use model. highlights adaptability validity environments, contributing more competent practices.

Language: Английский

Fabrication and thermal properties of composite phase change materials based on modified diatomite for thermal energy storage DOI
Jianan Yao,

Guangtong Zhang,

Yi Zhang

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 113, P. 115749 - 115749

Published: Feb. 8, 2025

Language: Английский

Citations

2
A review of the influencing factors of building energy consumption and the prediction and optimization of energy consumption DOI Creative Commons

Zhongjiao Ma,

Z. Yan,

M. He

et al.

AIMS energy, Journal Year: 2025, Volume and Issue: 13(1), P. 35 - 85

Published: Jan. 1, 2025

<p>Concomitant with the expeditious growth of construction industry, challenge building energy consumption has become increasingly pronounced. A multitude factors influence operations, thereby underscoring paramount importance monitoring and predicting such consumption. The advent big data engendered a diversification in methodologies employed to predict Against backdrop influencing operation consumption, we reviewed advancements research pertaining supervision prediction deliberated on more energy-efficient low-carbon strategies for buildings within dual-carbon context, synthesized relevant progress across four dimensions: contemporary state supervision, determinants optimization Building upon investigation three predictive were examined: (ⅰ) Physical methods, (ⅱ) data-driven (ⅲ) mixed methods. An analysis accuracy these revealed that methods exhibited superior precision actual Furthermore, predicated this foundation identified determinants, also explored prediction. Through an in-depth examination prediction, distilled pertinent accurate forecasting offering insights guidance pursuit conservation emission reduction.</p>

Language: Английский

Citations

0
Feature Optimization with Metaheuristics for Artificial Neural Network-Based Chiller Power Prediction DOI
Nor Farizan Binti Zakaria, Mohd Herwan Sulaiman, Zuriani Mustaffa

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112561 - 112561

Published: April 1, 2025

Language: Английский

Citations

0
Analyzing and Forecasting Laboratory Energy Consumption Patterns Using Autoregressive Integrated Moving Average Models DOI Open Access
Yitong Niu,

Xiongjie Jia,

Chee Keong Lee

et al.

Laboratories, Journal Year: 2024, Volume and Issue: 2(1), P. 2 - 2

Published: Dec. 30, 2024

This study applied ARIMA modeling to analyze the energy consumption patterns of laboratory equipment over one month, focusing on enhancing management in laboratory. By explicitly examining AC and DC equipment, this obtained detailed daily operating cycles periods inactivity. Advanced differencing diagnostic checks were used verify model accuracy white noise characteristics through enhanced Dickey–Fuller testing residual analysis. The results demonstrate model’s predicting consumption, providing valuable insights into use model. highlights adaptability validity environments, contributing more competent practices.

Language: Английский

Citations

2