Results of ECNN compared with CNN

Results of ECNN compared with CNN

Source publication
Conference Paper
Full-text available
Traditional grid moves toward Smart Grid (SG). In traditional grids, electricity was wasted in generation-transmission-distribution. SG is introduced to solve prior issues. In smart grids, how to utilize massive smart meter's data in order to improve and promote the efficiency and viability of both generation and demand side is a compelling issue....

Citations

... Liu et al. [15] also find that a sparse encoding network can improve the forecast for an LSTM at the household-level. Naeem et al. [90] develop a day-ahead load forecast of an Australian network-grid using an Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into Intrinsic Mode Functions (IMF) and residuals. These modes and residuals are passed onto a Denoising Auto Encoder (DAE) for feature extraction. ...
Article
The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.
... DL models can automate feature engineering, handle large and complex data [46,47,48]. Therefore, these models widely used in recent studies [49,50,51]. In [26,45], authors have proposed HNN for ET detection such as LSTM-MLP, W&D-CNN. ...
... from tabulate import tabulate 47 48 Read the Dataset from Google Drive as shown below, data.csv file is in 50 data folder 51 52 df = pd . read_csv ( '/ content / drive / My Drive / data / data . ...
Thesis
Electricity theft (ET) is a major problem in developing countries. It a�ects the economy that causes revenue loss. It also decreases the reliability and stability of electricity utilities. Due to these losses, the quality of supply e�ects and tari � imposed on legitimate consumers. ET is an essential part of Non-technical loss (NTL) and it is challenging for electricity utilities to �nd the responsible people. Several methodologies have developed to identify ET behaviors automatically. However, these approaches mainly assess records of consumers' electricity usage, may prove inadequate in detecting ET due to a variety of theft attacks and irregularity of consumers' behavior. Moreover, some important challenges are needed to be addressed. (i) The number of normal consumers has been wrongly identi�ed as fraudulent. This leads to high False-positive rate (FPR). After the detection of theft, on-site inspection is needed to validate the detected person, either is it fraudulent or not and it is costly. (ii) The imbalanced nature of datasets which negatively a�ect on the model's performance. (iii) The problem of over�tting and generalization error is often faced in deep learning models, predicts unseen data inaccurately. So, the motivation for this work to detect illegal consumers accurately. We have proposed four Arti�cial intelligence (AI) models in this thesis. In system model 1, we have proposed Enhanced arti�cial neural network blocks with skip connections (EANNBS). It makes training easier, reduces over�tting, FPR and generalization error, as well as execution time. Temporal convolutional network with enhanced multi-layer perceptron (TCN-EMLP) is proposed in system model 2. It analyzes the sequential data based on daily electricity-usage records, obtained from smart meters. At the same time, EMLP integrates the non-sequential auxiliary data, such as data related to electrical connection type, property area, electrical appliances usage, etc. System model 3 based on Residual network (RN) that is used to automate feature extraction while three tree-based classi�ers such as Decision tree (DT), Random forest (RF) and Adaptive boosting (AdaBoost) are trained on the obtained features for classi�cation. Hyperparameter tuning toolkit is presented in this system model, named as Hyperactive optimization toolkit. Bayesian is used as an optimizer in this toolkit that aims to simplify the tuning process of DT, RF and AdaBoost. In system model 4, input is forwarded to three di�erent and well-known Machine learning (ML) techniques, i.e., Support vector machine (SVM), as an input. At this stage, a meta-heuristic algorithm named Simulated annealing (SA) is employed to acquire optimal values for ML models' hyperparameters. Finally, ML models' outputs are used as features for meta-classi�ers to achieve �nal classi�cation with Light Gradient boosting machine (LGBM) and Multi-layer perceptron (MLP). Furthermore, Pakistan residential electricity consumption dataset (PRECON1), State grid corporation of china (SGCC2) and Commission for energy regulation (CER3) datasets is used in this thesis. SGCC dataset contains 9% fraudulent consumers, which are extremely less than non-fraudulent consumers, due to the imbalance nature of data. Furthermore, many classi�cation techniques have poor predictive class accuracy for the positive class. These techniques mainly focus on minimizing the error rate while ignoring the minority class. Many re-sampling techniques are used in literature to adjust the class ratio; however, sometimes, these techniques remove the important information that is necessary to learn the model and cause over�tting. By using six previous theft attacks, we generate theft cases to mimic the real world theft attacks in original data. We have proposed the combination of oversampling and under-sampling techniques that is Near miss borderline synthetic minority oversampling technique (NMB-SMOTE), Tomek link borderline synthetic minority oversampling technique with support vector machine (TBSSVM) and Synthetic minority oversampling technique with near miss (SMOTE-NM) to handle imbalanced classi�cation problem. We have conducted a comprehensive experiment using SGCC, CER and PRECON datasets. The performance of suggested model is validated using di�erent performance metrics that are derived from Confusion matrix (CM).
... Liu et al.[143] also nd that a sparse encoding network can improve the forecast for an LSTM at the household-level. Naeem et al.[164] develop a day-ahead load forecast of an Australian network-grid using an Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into Intrinsic Mode Functions (IMF) and residuals. These modes and residuals are passed onto a Denoising Auto Encoder (DAE) for feature extraction. ...
Preprint
Full-text available
The increased digitalisation and monitoring of the energy system opens up numerous opportunities % and solutions which can help to decarbonise the energy system. Applications on low voltage (LV), localised networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known LV level open datasets to encourage further research and development.
Article
Full-text available
Achieving the desired accuracy in time series forecasting has become a binding domain, and developing a forecasting framework with a high degree of accuracy is one of the most challenging tasks in this area. Combining different forecasting methods to construct efficient hybrid models has been widely reported in the literature regarding this challenge. Various types of hybrid models have been developed and successfully employed to improve forecasting accuracy. The well-known hybrid models can be generally categorized into four classes: (1) preprocessing-based, (2) parameter optimization-based, (3) components combination-based, and (4) postprocessing-based hybrid models. Despite the significant successes of hybrid models, efforts to access more accurate results face continued growth. Hybridization of hybrid models is a novel idea proposed to obtain extreme accuracy in recent literature, in which two or more hybrid classes are combined instead of conjoining the conventional individual forecasting methods. Although, in many publications, the aforementioned classes of hybrid models have been reviewed and analyzed in a wide variety of forecasting fields; no study is conducted to review the hybridization of hybrid models. This paper’s main contribution is to fill this gap and provide classification and comprehensive review of the current endeavors done in the hybridization of hybrid models in time series forecasting areas. Our searches indicate that more than 250 papers have been published in recent years utilizing hybridization of hybrid models. In this paper, these published papers have been classified regarding their different used combination strategies into four main categories, including (1) Hybridization with preprocessing-based hybrid models (HPH), (2) Hybridization with parameter optimization-based hybrid models (HOH), (3) Hybridization with components combination-based hybrid models (HCH) and, (4) Hybridization with postprocessing-based hybrid models (HSH). Each hybridization of the hybrid class is evaluated regarding the usage frequency, specific merits, and limitations. It can be inferred from reviewing articles that the hybridization of the hybrid concept, as a recent advancement in time series forecasting, can significantly improve traditional hybrid models’ accuracy. Furthermore, each category’s research gaps and some future research directions are identified in this paper.