The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.

A review on the prediction of building energy consumption

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review of data-driven building energy consumption prediction studies

The gap between predicted and measured energy performance of buildings: A framework for investigation

Infrared thermography for condition monitoring – A review

Active infrared thermography is a fast and accurate non-destructive evaluation technique that is of particular relevance to the aerospace industry for the inspection of aircraft and helicopters’ primary and secondary structures, aero-engine parts, spacecraft components and its subsystems. This review provides an exhaustive summary of most recent active thermographic methods used for aerospace applications according to their physical principle and thermal excitation sources. Besides traditional optically stimulated thermography, which uses external optical radiation such as flashes, heaters and laser systems, novel hybrid thermographic techniques are also investigated. These include ultrasonic stimulated thermography, which uses ultrasonic waves and the local damage resonance effect to enhance the reliability and sensitivity to micro-cracks, eddy current stimulated thermography, which uses cost-effective eddy current excitation to generate induction heating, and microwave thermography, which uses electromagnetic radiation at the microwave frequency bands to provide rapid detection of cracks and delamination. All these techniques are here analysed and numerous examples are provided for different damage scenarios and aerospace components in order to identify the strength and limitations of each thermographic technique. Moreover, alternative strategies to current external thermal excitation sources, here named as material-based thermography methods, are examined in this paper. These novel thermographic techniques rely on thermoresistive internal heating and offer a fast, low power, accurate and reliable assessment of damage in aerospace composites.

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Recent Advances in Active Infrared Thermography for Non-Destructive Testing of Aerospace Components.

This paper describes the quantitative determination of the sizes and locations of subsurface defects using lock-in infrared thermography. A phase (or temperature) difference between the defect area and the healthy area indicates the qualitative location and size of the defect. To accurately estimate these parameters, the shearing-phase technique has been employed, where the inspected image is shifted by a certain number of pixels to obtain a shifted image while subtraction of one image from the other gives the shearing-phase distribution. The shearing-phase distribution has maximum, minimum, and zero points that help determine quantitatively the size and location of the subsurface defect. Experimental results for a steel plate with artificial subsurface defects show good agreement with actual values.

Quantitative determination of a subsurface defect of reference specimen by lock-in infrared thermography

In this study, the investigation on lock-in infrared thermography was done for the detection and estimation of artificial subsurface defects size and depth in stainless steel sample. The experimental and the finite element analysis were performed at several excitation frequencies to interrogate the sample ranging from 0.182 down to 0.021 Hz. A finite element model using ‘ANSYS 14.0’ was used to completely simulate the lock-in thermography. The four point method was used in post processing of every pixel of thermal images using the MATLAB programming language. A signal to noise ratio analysis was performed on both phase and amplitude images in each excitation frequency to determine the optimum frequency. The relationship of the phase value with respect to excitation frequency and defect depths was examined. Amplitude image was quantitatively analyzed using Vision Assistant, a special tool in LABVIEW program to acquire the defects size. The phase image was used to calculate the defects depth considering the thermal diffusivity of the material and the excitation frequency for which the defects become visible. A finite element analysis result was found to have good correlation with experimental result and thus demonstrated potentiality in quantification of subsurface defects.

Investigation of lock-in infrared thermography for evaluation of subsurface defects size and depth

Evaluation of coating thickness by thermal wave imaging: A comparative study of pulsed and lock-in infrared thermography – Part I: Simulation

Evaluation of coating thickness by thermal wave imaging: A comparative study of pulsed and lock-in infrared thermography – Part II: Experimental investigation

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

https://www.mdpi.com/1424-8220/19/9/2025/pdf

Wontae Kim

Papers

Quantitative determination of a subsurface defect of reference specimen by lock-in infrared thermography

Investigation of lock-in infrared thermography for evaluation of subsurface defects size and depth

Evaluation of coating thickness by thermal wave imaging: A comparative study of pulsed and lock-in infrared thermography – Part I: Simulation

Evaluation of coating thickness by thermal wave imaging: A comparative study of pulsed and lock-in infrared thermography – Part II: Experimental investigation

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework