This page has only limited features, please log in for full access.

Mrs. Elsa Nisa
National Chin-Yi University of Technology

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Deep Learning
0 HVAC
0 Machine Learning
0 Refrigeration
0 neural network

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 14 January 2021 in Sustainability
Reads 0
Downloads 0

Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R2), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R2, 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value.

ACS Style

Elsa Chaerun Nisa; Yean-Der Kuan. Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms. Sustainability 2021, 13, 744 .

AMA Style

Elsa Chaerun Nisa, Yean-Der Kuan. Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms. Sustainability. 2021; 13 (2):744.

Chicago/Turabian Style

Elsa Chaerun Nisa; Yean-Der Kuan. 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms." Sustainability 13, no. 2: 744.