Generalized linear model-based data analytic approach for construction equipment management

Yang, Yanwen; Yu, Chen; Zhong, Ray Y.

doi:10.1016/j.aei.2023.101884

Cited by 6 publications

(1 citation statement)

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“…R 2 represents the proportion of the variance in the energy usage data that is predictable by the model. A higher R 2 value indicates a better fit of the model to the data [45].…”

Section: F Data Analysismentioning

confidence: 99%

Deep Learning Approaches with Optimum Alpha for Energy Usage Forecasting

Wibawa,

Utama,

Akbari

et al. 2023

Knowl. Eng. Data Sci.

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Energy use is an essential aspect of many human activities, from individual to industrial scale. However, increasing global energy demand and the challenges posed by environmental change make understanding energy use patterns crucial. Accurate predictions of future energy consumption can greatly influence decision-making, supply-demand stability and energy efficiency. Energy use data often exhibits time-series patterns, which creates complexity in forecasting. To address this complexity, this research utilizes Deep Learning (DL), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) models. The main objective is to improve the accuracy of energy usage forecasting by optimizing the alpha value in exponential smoothing, thereby improving forecasting accuracy. The results showed that all DL methods experienced improved accuracy when using optimum alpha. LSTM has the most optimal MAPE, RMSE, and R2 values compared to other methods. This research promotes energy management, decision-making, and efficiency by providing an innovative framework for accurate forecasting of energy use, thus contributing to a sustainable and efficient energy system.

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“…R 2 represents the proportion of the variance in the energy usage data that is predictable by the model. A higher R 2 value indicates a better fit of the model to the data [45].…”

Section: F Data Analysismentioning

confidence: 99%