A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques

Mares, Jamer Jiménez; Navarro, Loraine; M., Christian G. Quintero; Pardo, Mauricio

doi:10.3390/en13164040

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…Finally, the third stage allows correcting the base curves considering external variables (e.g., weather conditions). The authors explain the stages in [28].…”

Section: B Model Assemblymentioning

confidence: 99%

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An Architecture to Improve Energy-Related Time-Series Model Validity Based on the Novel rMAPE Performance Metric

et al. 2023

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In this paper, an architecture based on computational intelligence for time series modeling is proposed to guarantee the automatic adjustability of trained models no matter the dynamic behavior of the modeled phenomena. The proposed method can assess the performance; and then proposes a maintenance routine for the time-series model. Thus, an auditor is devised to identify when a model must be updated before losing forecast performance. It has been determined that the MAPE (Mean Absolute Percentage Error) metric could not reveal changes in the model predicted curve contributing to invalidating the model, in particular if a non-stationary behavior is expected in the studied phenomena. Therefore, the novel rMAPE performance metric is proposed, so that the auditor does detect that the updating process does not achieve better performance; the system opts for replacing the time-series modeling techniques included in an available knowledge base. The intelligent system allows building time-series models automatically considering exogenous variables such as weather, calendar, and statistical transformations that can lead to the number of models required for a particular application. The proposed approach has been experimentally tested for power consumption and energy price via simulation. The forecasting results showed an improvement in the MAPE of up to 23% in the tests performed.INDEX TERMS. Forecasting, intelligent systems, time series modeling.

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“…Finally, the third stage allows correcting the base curves considering external variables (e.g., weather conditions). The authors explain the stages in [28].…”

Section: B Model Assemblymentioning

confidence: 99%

“…A complete description of the implementation of the atypicalcurve identification, base-curve generation, and base-curve intelligent correction can be found in [28]. Figure 5 shows a summary of each required stage of the forecasting models.…”

Section: B Model Block Implementationmentioning

confidence: 99%

An Architecture to Improve Energy-Related Time-Series Model Validity Based on the Novel rMAPE Performance Metric

et al. 2023

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“…In 2020, J.J. Mares et al [33] proposed a hierarchical clustering-based short-term forecasting method for energy demand. This method employs dynamic time warping as the similarity metric and integrates it with artificial neural networks (ANNs).…”

Section: Basic Technology Developmentmentioning

confidence: 99%

A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings

Jiao

Liu

et al. 2023

Sustainability

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This article aims to systematically summarize the methods for intelligent operation of large public buildings, the integration and application of related technologies, as well as their development trends and challenges. (1) Background: In response to the rapid development and future needs of intelligent operation and maintenance, this study summarizes the development process of intelligent operation and maintenance in building operations, as well as relevant technical achievements and challenges; (2) Method: Quantitative and qualitative bibliometric statistical methods were used for overall analysis; (3) Result: Based on system theory, a B-IRO model was developed, and the current status of intelligent operation- and maintenance-related technologies and applications was sorted out. A framework for the entire industry was established, and future development trends were proposed as further research directions.

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A hybrid K-means and KNN approach for enhanced short-term load forecasting incorporating holiday effects

Hnin,

Karnjana,

Kohda

et al. 2024

Energy Reports

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A Methodology for Energy Load Profile Forecasting Based on Intelligent Clustering and Smoothing Techniques

Cited by 7 publications

References 18 publications

An Architecture to Improve Energy-Related Time-Series Model Validity Based on the Novel rMAPE Performance Metric

An Architecture to Improve Energy-Related Time-Series Model Validity Based on the Novel rMAPE Performance Metric

A Review of Theory and Application Development of Intelligent Operation Methods for Large Public Buildings

A hybrid K-means and KNN approach for enhanced short-term load forecasting incorporating holiday effects

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