Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Himeur, Yassine; Ghanem, Khalida; Alsalemi, Abdullah; Bensaali, Fayçal; Amira, Abbes

doi:10.1016/j.apenergy.2021.116601

Cited by 308 publications

(136 citation statements)

References 101 publications

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“…Hardware methods rely on physical devices; however, their limitations are deployment cost, devices failure in harsh weather conditions, and physical devices need batteries that are sometimes difficult to maintain 11 . Recently, data‐driven artificial intelligence (AI) methods have drawn the attention from researchers 24 . In Reference 12, the authors proposed a semi‐supervised learning mechanism to detect ET.…”

Section: Related Workmentioning

confidence: 99%

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Arif

Javaid

Aldegheishem

et al. 2021

Concurrency and Computation

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Electricity theft (ET) causes major revenue loss in power utilities. It reduces the quality of supply, raises production cost, causes legal consumers to pay the higher cost, and impacts the economy as a whole. In this article, we use the State Grid Corporation of China (SGCC) dataset, which contains electricity consumption data of 1035 days for two classes: normal and fraudulent. In this work, ET detection model is proposed that consists of four steps: interpolation, data balancing, feature extraction, and classification. First, missing values of the dataset are recovered using the interpolation method. Second, resampling technique is implemented. ET consumers are 9% in the SGCC dataset that make the model inefficient to correctly classify both classes (normal and theft). A hybrid resampling technique is proposed, named synthetic minority oversampling technique with near miss. Third, residual network extracts the latent features from the SGCC dataset. Fourth, three tree based classifiers, such as decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost) are applied to train the encoded feature vectors for classification. Besides, search for good hyperparameters is a challenging task, which is usually done manually and takes a considerable amount of time. To resolve this problem, Bayesian optimizer is used to simplify the tuning process of DT, RF, and AdaBoost. Finally, the results indicate that RF outperforms DT and AdaBoost.

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Section: Related Workmentioning

confidence: 99%

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Arif

Javaid

Aldegheishem

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Comprehensive taxonomy is used to classify existing algorithms based on different modules and parameters adopted. The effectiveness of the anomaly detection technology before deriving future directions is attracting significant attention to the current trends in the abnormal power consumption 24 . The spatio‐temporal dependencies of the biosphere variables are handled by applying feature extraction steps in the grid independently and then followed by spatio‐temporal event detection.…”

Section: Related Workmentioning

confidence: 99%

Real time identification of anomalous events in coastal regions using deep learning techniques

Varalakshmi

Murugaiyan²,

Ravichandran

et al. 2021

Concurrency and Computation

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Summary The present existing weather prediction systems deployed by weather forecasting agencies predict the anomalous events using numerical simulation models developed based on geophysics and oceanographic theories. The major disadvantage with existing models is that are explicit simulation models. So, we try to build a real time prediction system that will automatically predict the intensity of any marine natural disaster events and alert the authorities to take necessary actions using a satellite and observational real time data set including moored met ocean data. We propose a novel hybrid model which is a combination of Convolutional Neural Network (CNN) and Long Short‐Term Memory (LSTM). The paper analyzes various other models such as Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi‐LSTM) Recurrent Neural Network (RNN), and Extreme Learning Machine. In addition the other standard CNN architectures are also analyzed. Since the residual CNN network is the most effective architecture, the paper proposes a modification of the residual CNN network by combining the residual CNN network with LSTM/GRU RNN network along with a proposed new activation function which was experimentally proved to be more effective than the deep learning community standard activation function ReLU. The proposed new activation function overcomes the dead neuron propagation problem in ReLU activation function and hence the model's accuracy is increased by more than 1%. The proposed CNN + LSTM/GRU model is benchmarked with UCR time series benchmark dataset and it is experimentally seen that the model performs better than all existing algorithms.

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“…Anomaly detection refers to distinguishing between normal and abnormal samples in data [43,44]. Anomaly detection mainly detects anomalous data or situations in various fields, such as manufacturing, medical care, and image processing [45][46][47][48][49][50]. The difficulty facing anomaly detection is that the frequency of occurrence of abnormal data is significantly lower than normal.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

Kang

Kim

et al. 2021

Applied Sciences

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This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning require deep learning–based problem solving in various fields. Accordingly, various research efforts use deep learning technology to detect errors, such as product defects and diseases, in depth images. However, a depth image expressed in grayscale has limited information, compared with a three-channel image with potential colors, shapes, and brightness. In addition, in the case of tires, despite the same defect, they often have different sizes and shapes, making it difficult to train deep learning. Therefore, in this paper, the four-step process of (1) image input, (2) highlight image generation, (3) image stacking, and (4) image training is applied to a deep learning segmentation model that can detect atypical defect data. Defect detection aims to detect vent spews that occur during tire manufacturing. We compare the training results of applying the process proposed in this paper and the general training result for experiment and evaluation. For evaluation, we use intersection of union (IoU), which compares the pixel area where the actual error is located in the depth image and the pixel area of the error inferred by the deep learning network. The results of the experiment confirmed that the proposed methodology improved the mean IoU by more than 7% and the IoU for the vent spew error by more than 10%, compared to the general method. In addition, the time it takes for the mean IoU to remain stable at 60% is reduced by 80%. The experiments and results prove that the methodology proposed in this paper can train efficiently without losing the information of the original depth data.

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Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Cited by 308 publications

References 101 publications

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Big data analytics for identifying electricity theft using machine learning approaches in microgrids for smart communities

Real time identification of anomalous events in coastal regions using deep learning techniques

Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing

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