Simplified Building Thermal Model Development and Parameters Evaluation Using a Stochastic Approach

Boodi, Abhinandana; Beddiar, Karim; Amirat, Yassine; Benbouzid, Mohamed

doi:10.3390/en13112899

Cited by 28 publications

(18 citation statements)

References 37 publications

(41 reference statements)

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“…The authors introduced a method in [18] to estimate the demand patterns of the building based on space heating and appliances category by utilizing various factors. Similarly, [19] developed a technique to simplify the physical characteristics of the system by using frequency features analysis. These methods worked well for energy forecasting and demand but their huge cost computation and time complexity make it problematic to generalize these techniques for real-time energy forecast applications [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…We utilized various assessment metrics such as mean square error (MSE), root means square error (RMSE), mean average percentage error (MAPE) and mean absolute error (MAE) to evaluate the performance of the proposed system. A mathematical representation of all these assessment metrics is expressed in Equation (19)(20)(21)(22). The MAE metric reports the difference of the predicted variables in percentage and variation among predicted and testing variables are represented by RMSE.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

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Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

2021

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Industrial and building sectors demand efficient smart energy strategies, techniques of optimization, and efficient management for reducing global energy consumption due to the increasing world population. Nowadays, various artificial intelligence (AI) based methods are utilized to perform optimal energy forecasting, different simulation tools, and engineering methods to predict future demand based on historical data. Nevertheless, nonlinear energy demand modeling is still unfledged for a better solution to handle short-term and long-term dependencies and avoid static nature because it is purely on historical datadriven. In this paper, we propose an ensemble deep learning-based approach to predict and forecast energy demand and consumption by using chronological dependencies. Our system initially processes the data, cleaning, normalization, and transformation to ensure the model performance. Furthermore, the preprocess data is fed to propose the ensemble model to extract hybrid discriminative features by using convolution neural network (CNN), stacked, and bi-directional long-short term memory (LSTM) architectures. We trained our proposed system on the historical data to forecast the energy demand and consumption with a different time interval. In the proposed technique, we utilized the concept of active learning based on moving windows to ensure and improve the prediction performance of the system. The proposed system could be applicable to employ energy consumption in industrial and building sectors to demonstrate their significance and effectiveness. We evaluated the proposed system by using benchmark, residential UCI, and local Korean commercial building datasets. We conducted different extensive experimentation to show the error rate and used various kinds of evaluation matrices, which indicate the lower error rate of the proposed system.

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Section: Literature Reviewmentioning

confidence: 99%

Section: B Evaluation Metricsmentioning

confidence: 99%

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

2021

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“…Furthermore, these real-time controllers are model-based controllers and their performance accuracy is greatly dependent on the building model accuracy. Low-order models that can replicate the thermal dynamics of a building with high accuracy and low computational cost are useful for model-based controllers [27]. However, due to all these reasons the industrial application of such multi-objective controllers is still difficult and expensive.…”

Section: Introductionmentioning

confidence: 99%

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

2022

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The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

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“…Therefore, reduction of building energy consumption is crucial for sustainable development, to minimize its impact on global warming and climate change. Nevertheless, the comfortability and energy consumption are directly proportional to each other [13]. Hence, a rational approach is required to minimize the energy consumption by providing maximum comfort.…”

Section: Introductionmentioning

confidence: 99%

Building Occupancy Behavior and Prediction Methods: A Critical Review and Challenging Locks

et al. 2021

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Energy use in buildings is increasing to provide optimal comfort for the occupants. People spend 90% of their lifetime in buildings. Therefore, indoor environment quality and comfort management have a crucial role in maintaining occupants' health and productivity. Reducing energy consumption for optimal comfort management is important to minimize CO 2 emission and global warming by the building sector. According to the literature, it is possible to control and reduce energy consumption by monitoring occupants' behavior and estimating the number of occupants. A critical review is carried out in this paper to analyze the existing methodologies for modeling occupant behavior and prediction with respect to comfort and energy management. A comprehensive analysis is also performed on recent developments and challenges in modeling, along with recommendations and future perspectives.

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Simplified Building Thermal Model Development and Parameters Evaluation Using a Stochastic Approach

Cited by 28 publications

References 37 publications

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Building Occupancy Behavior and Prediction Methods: A Critical Review and Challenging Locks

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