Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

https://www.mdpi.com/2079-9292/8/2/122/pdf

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

Demand side management (DSM) is an essential property of smart grid systems. Along with increasing expectations related to power quality from customers, and as new types of loads emerge, such as electric vehicles, local (renewable) energy generation, and stationary and mobile energy storage, it is critical to develop new methods for DSM. In this paper, we first construct a more efficient and reliable communication infrastructure in smart grid based on cognitive radio technology, which is an essential component for enabling DSM. Then, we propose a distributed energy storage planning approach based on game algorithm in DSM, which helps users select the appropriate size of storage units for balancing the cost in the planning period and during its use. Since planning problems may lead to consumer discomfort, we propose a cost function consisting of the billing, generation costs, and discomfort costs to balance users’ preferences with the payment. Furthermore, a game theory-based distributed energy management scheme is developed in DSM without leaking user privacy, which is used as inner optimization in our proposed distributed energy storage planning approach. In this energy management scheme, Nash equilibrium is obtained with minimum information exchange using proximal decomposition algorithm. Simulation results show superior performance of our proposed DSM mechanism in reducing the peak-to-average ratio, total cost, user’s daily payment, and energy consumption in smart grid communication networks.

Green Energy Scheduling for Demand Side Management in the Smart Grid

This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy of ~ 90%, whilst operating through dense smoke.

https://dl.acm.org/doi/pdf/10.1145/3386901.3388945

See through smoke: robust indoor mapping with low-cost mmWave radar

Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning long-term memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using real-world workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads.

/pdf/towards-accurate-prediction-for-high-dimensional-and-highly-28nai1l1ah.pdf

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

Electricity price forecasting is a significant part of smart grid because it makes smart grid cost efficient. Nevertheless, existing methods for price forecasting may be difficult to handle with huge price data in the grid, since the redundancy from feature selection cannot be averted and an integrated infrastructure is also lacked for coordinating the procedures in electricity price forecasting. To solve such a problem, a novel electricity price forecasting model is developed. Specifically, three modules are integrated in the proposed model. First, by merging of Random Forest (RF) and Relief-F algorithm, we propose a hybrid feature selector based on Grey Correlation Analysis (GCA) to eliminate the feature redundancy. Second, an integration of Kernel function and Principle Component Analysis (KPCA) is used in feature extraction process to realize the dimensionality reduction. Finally, to forecast price classification, we put forward a differential evolution (DE) based Support Vector Machine (SVM) classifier. Our proposed electricity price forecasting model is realized via these three parts. Numerical results show that our proposal has superior performance than other methods.

Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid

Voice-user interface (VUI) has become an integral component in modern personal devices (\textite.g., smartphones, voice assistant) by fundamentally evolving the information sharing between the user and device. Acoustic sensing for VUI is designed to sense all acoustic objects; however, the existing VUI mechanism can only offer low-quality speech sensing. This is due to the audible and inaudible interference from complex ambient noise that limits the performance of VUI by causing denial-of-service (DoS) of user requests. Therefore, it is of paramount importance to enable noise-resistant speech sensing in VUI for executing critical tasks with superior efficiency and precision in robust environments. To this end, we investigate the feasibility of employing radio-frequency signals, such as millimeter wave (mmWave) for sensing the noise-resistant voice of an individual. We first perform an in-depth study behind the rationale of voice generation and resulting vocal vibrations. From the obtained insights, we presentWaveEar, an end-to-end noise-resistant speech sensing system.WaveEar comprises a low-cost mmWave probe to localize the position of the speaker among multiple people and direct the mmWave signals towards the near-throat region of the speaker for sensing his/her vocal vibrations. The received signal, containing the speech information, is fed to our novel deep neural network for recovering the voice through exhaustive extraction. Our experimental evaluation under real-world scenarios with 21 participants shows the effectiveness ofWaveEar to precisely infer the noise-resistant voice and enable a pervasive VUI in modern electronic devices.

https://dl.acm.org/doi/pdf/10.1145/3307334.3326073

Chenhan Xu

Papers

Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid

WaveEar: Exploring a mmWave-based Noise-resistant Speech Sensing for Voice-User Interface