Entropy Application for Forecasting

López-Menéndez, Ana Jesús; Pérez-Suárez, Rigoberto

doi:10.3390/e22060604

Cited by 4 publications

(1 citation statement)

References 12 publications

(17 reference statements)

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“…A higher entropy suggests more complexity and variability, while lower entropy indicates a more predictable and less complex dataset [29]. The formula of entropy without specific category data involves calculating the entropy of a probability distribution, where each probability represents the likelihood of an event occurring [30]. The generic formula for entropy (H) in this context is:…”

Section: Time Interval Analysismentioning

confidence: 99%

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

2024

IJIES

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The purpose of this study is to introduce a novel approach to predict induction motor frequency adjustments in Air Handling Units (AHU). This is essential as traditional methods have frequently been unable to effectively address the complex seasonal and stochastic fluctuations that are inherent in these environments. To overcome this challenge, the research focuses on utilizing experimental Chen's Fuzzy Time Series model, specifically designed to incorporate temporal and seasonal patterns into the predictive analysis. Various predictive models, including Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters Exponential Smoothing (HWES), neural network (NN) based ensemble model, hybrid of artificial neural network (ANN) and SARIMA, and Seasonal Autoregressive Integrated Moving Average with Exogenous factors (SARIMAX), are compared to determine the most effective model in optimizing AHU induction motor frequency. Results indicate that the modified Chen's Fuzzy Time Series model demonstrated high efficacy with an R-squared value of 0.9945 in a one-hour time interval in the seasonal pattern, showing an almost perfect fit between predicted outcomes and actual data compared to other prediction models. Furthermore, the modified Chen's model achieved a Mean Absolute Percentage Error (MAPE) of 2.41% and a Root Mean Square Error (RMSE) of 0.72, significantly outperforming other models in predictive accuracy and reliability. The modified Chen's model showed an efficiency improvement of 86% in MAPE and 87% in RMSE compared to other prediction models.

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Section: Time Interval Analysismentioning

confidence: 99%