scholarly journals Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8325
Author(s):  
Gustavo Felipe Martin Nascimento ◽  
Frédéric Wurtz ◽  
Patrick Kuo-Peng ◽  
Benoit Delinchant ◽  
Nelson Jhoe Batistela
Keyword(s):  
Power Consumption ◽  
Outlier Detection ◽  
Forecast Error ◽  
Electricity Consumption ◽  
Energy Transition ◽  
Detection Methods ◽  
Total Consumption ◽  
Consumption Data ◽  
Reduce Energy Consumption ◽  
The Difference

Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption.

Electricity Consumption Data Analysis Using Various Outlier Detection Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 12-27
Author(s):  
Sidi Mohammed Kaddour ◽  
Mohamed Lehsaini
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Outlier Detection ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Efficient Solutions ◽  
Detection Methods ◽  
Energy Field ◽  
Consumption Data ◽  
Peak Energy

Nowadays, detecting abnormal power consumption behavior of householders has become a big concern in the smart energy field; overcoming this limitation will help in identifying efficient solutions to reduce power consumption. This paper proposes a new methodology for detecting abnormal energy consumption in residential buildings based on hourly readings of energy consumption and peak energy consumption. The proposition is implemented using three unsupervised outlier detection methods (isolation forest, one-class SVM, and k-means). The authors propose this solution to help residents in reducing operating costs by detecting consumption failures that cannot be detected easily. On the other hand, energy providers will have the access to detailed data about anomalies, faulty appliances, and houses with poor power control strategy in general, which will help in pinpointing overconsumption problems, thus enhancing human awareness and reducing energy consumption.

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

Development of Smart Plug Using Power Line Communication

2015 ◽  
Vol 781 ◽  
pp. 357-360
Author(s):  
Naruephon Srihara ◽  
Manop Wongsaisuwan
Keyword(s):  
Power Consumption ◽  
Electricity Consumption ◽  
Power Line ◽  
Smart Device ◽  
Power Line Communication ◽  
Turn On ◽  
Home Appliances ◽  
Consumption Data ◽  
Main Components ◽  
Current Energy

This paper describes the development of Smart Plug using power line communication. The smart plug system is comprised of two main components which are Smart Plug and Data Concentrator. Smart Plug is a wall outlet which is able to measure power consumption and to control (turn on/off) the connected load via UDP protocol. Smart plug will measure the voltage, current, energy and power consumption of electric home appliances that are connected to it and broadcast these data over the distributed power wires. Data Concentrator is a center of data accessing between the user and each of the smart plug in the system. System owners can access the system to get the electricity consumption data in their system or to control the operation of electric home appliances that are connected to any smart plug by a personal computer or a smart device. Each smart plug has 16-bit unique ID for address identification in the system so this system can support up to 65,536 smart plugs.

scholarly journals Modelling Electricity Consumption Forecasting Using the Markov Process and Hybrid Features Selection

2021 ◽  
Vol 13 (5) ◽  
pp. 14-23
Author(s):  
Hadis Dalkani ◽  
◽  
Musa Mojarad ◽  
Hassan Arfaeinia
Keyword(s):  
Markov Process ◽  
Power Consumption ◽  
Power Distribution ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Features Selection ◽  
Hybrid Features ◽  
Consumption Data ◽  
Distribution Company ◽  
Time Variables

Given the problem of electrical energy storage, it is critical to predict the amount of load required in order to have a reliable and stable power distribution network. Predicting electricity consumption of subscribers and analyzing their consumption behavior under the influence of various factors and time variables is important. Given the large volume of subscriber consumption data and the effective factors, it is only possible to analyze the data using new information technology tools such as data mining. In this paper, feature selection, clustering and Markov process techniques are used to model and predict the power consumption data of subscribers. First, the selection of a subset of effective features is based on the combined PCA approach and the Firefly algorithm. Subscribers are then clustered based on the features selected by the K-means. Finally, subscriber behavior patterns are modeled to predict consumption using the Markov process on high-risk clusters. This study is simulated based on the data of electricity subscribers in Bushehr-Iran Power Distribution Company. The simulation results show the superiority of the proposed model over other similar algorithms such as LASSO-QRNN and HyFIS. The accuracy of power consumption prediction in the proposed method is about 1% compared to LASSO-QRNN and about 0.5% compared to HyFIS.

scholarly journals NILM techniques applied to a real-time monitoring system of the electricity consumption

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 139
Author(s):  
Barbara Cannas ◽  
Sara Carcangiu ◽  
Daniele Carta ◽  
Alessandra Fanni ◽  
Carlo Muscas ◽  
...  
Keyword(s):  
Power Consumption ◽  
Monitoring System ◽  
Event Detection ◽  
Electricity Consumption ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Detection Approach ◽  
Consumption Data ◽  
Load Monitoring ◽  
Using Data

Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

scholarly journals Abnormal electricity detection with hybrid deep neural network model

2018 ◽  
Vol 189 ◽  
pp. 03001
Author(s):  
Jie Liu ◽  
Xiang Cao ◽  
Diangang Wang ◽  
Kejia Pan ◽  
Cheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Large Scale ◽  
Deep Neural Network ◽  
Electricity Consumption ◽  
The State ◽  
Detection Model ◽  
Gradient Model ◽  
The Arts ◽  
The Difference

This paper tackles a new challenge in abnormal electricity detection: how to promptly detect stealing electricity behavior by a large-scale data from power users. Proposed scheme firstly forms power consumption gradient model by extracting daily trend indicators of electricity consumption, which can exactly reflect the short-term power consumption trend for each user. Furthermore, we design the line-losing model by analyzing the difference between power supplying and actual power consumption. Finally, a hybrid deep neural network detection model is built by combining with the power consumption gradient model and the line-losing model, which can quickly pin down to the abnormal electricity users. Comprehensive experiments are implemented by large-scale user samples from the State Grid Corporation and Tensorflow framework. Extensive results show that comparing with the state-of-the-arts, proposed scheme has a superior detection performance, and therefore is believed to be able to give a better guidance to abnormal electricity detection.

scholarly journals An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2060 ◽  
Author(s):  
Yajing Gao ◽  
Shixiao Guo ◽  
Jiafeng Ren ◽  
Zheng Zhao ◽  
Ali Ehsan ◽  
...  
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Large Scale ◽  
Operating Cost ◽  
Electricity Consumption ◽  
External Factors ◽  
Electric Bus ◽  
Consumption Data ◽  
Power Consumption Prediction ◽  
Consumption Prediction

With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

scholarly journals Electricity Safety Analysis and Improvement Based on User Classification

2020 ◽  
Vol 185 ◽  
pp. 01025
Author(s):  
Hongxia Zhu ◽  
Qing Wang ◽  
Congcong Li ◽  
Yan Du ◽  
Chao Yu
Keyword(s):  
Power Consumption ◽  
Electricity Consumption ◽  
Safety Analysis ◽  
Consumption Behavior ◽  
User Classification ◽  
Consumption Data ◽  
Different Types ◽  
User Groups ◽  
Consumption Curve

Analyzing electricity consumption behavior by classifying user groups and knowing each group’s characteristics are an important way to realize intelligent power consumption. This paper firstly classifies different types of users based on power consumption data and grasps the power consumption behavior of typical users. The main causes of accidents and alarms are then discussed based on the recorded events and power consumption curve. Finally, the corresponding solutions, which take the user’s social attributes and electricity consumption behavior into consideration are put forward to avoid the potential accidents.

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