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 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 Power Consumption Predicting and Anomaly Detection Based on Transformer and K-Means

2021 ◽  
Vol 9 ◽  
Author(s):  
Junfeng Zhang ◽  
Hui Zhang ◽  
Song Ding ◽  
Xiaoxiong Zhang
Keyword(s):  
Power Consumption ◽  
Electric Energy ◽  
Electrical Energy ◽  
Daily Lives ◽  
Electric Energy Consumption ◽  
Proposed Model ◽  
Consumption Data ◽  
Clustering Approach ◽  
Transformer Model ◽  
Deep Learning Model

With the advancement of technology and science, the power system is getting more intelligent and flexible, and the way people use electric energy in their daily lives is changing. Monitoring the condition of electrical energy loads, particularly in the early detection of aberrant loads and behaviors, is critical for power grid maintenance and power theft detection. In this paper, we combine the widely used deep learning model Transformer with the clustering approach K-means to estimate power consumption over time and detect anomalies. The Transformer model is used to forecast the following hour’s power usage, and the K-means clustering method is utilized to optimize the prediction results, finally, the anomalies is detected by comparing the predicted value and the test value. On real hourly electric energy consumption data, we test the proposed model, and the results show that our method outperforms the most commonly used LSTM time series model.

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.

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 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.

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.

scholarly journals Audit Energi dan Analisis Peluang Penghematan Energi Listrik Pada Bagian Produksi di PT. EPFM Makassar

2019 ◽  
Vol 17 (1) ◽  
pp. 42
Author(s):  
Jamal Jamal ◽  
Marlina Marlina ◽  
Floransya Dwi
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy Audit ◽  
Consumption Data ◽  
The Right ◽  
Consumption Intensity

Basic electricity tariffs that continue to increase force various parties to race to carry out savings programs, the right thing to apply the savings program is energy management and one of them is an energy audit. The energy audit carried out in this study was an energy audit at PT. Makassar EPFM. The energy audit starts with the collection and processing of energy consumption data at the factory, calculates the Energy Consumption Intensity (IKE). From the results of the calculation of the intensity of energy consumption it is known that the level of efficiency in the use of electrical energy in the building. The efficiency of electricity consumption at PT. EPFM can be improved

Thermal/electric energy generation and CO2 production for greenhouse facilities

2019 ◽  
Vol 2 (3) ◽  
pp. 141-151
Author(s):  
O. E. Gnezdova ◽  
E. S. Chugunkova
Keyword(s):  
Carbon Dioxide ◽  
Power Distribution ◽  
Power Plants ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Vegetable Crops ◽  
Carbon Dioxide Injection ◽  
Electricity Rates ◽  
Traditional Patterns ◽  
Generation Power

Introduction: greenhouses need microclimate control systems to grow agricultural crops. The method of carbon dioxide injection, which is currently used by agricultural companies, causes particular problems. Co-generation power plants may boost the greenhouse efficiency, as they are capable of producing electric energy, heat and cold, as well as carbon dioxide designated for greenhouse plants.Methods: the co-authors provide their estimates of the future gas/electricity rates growth in the short term; they have made a breakdown of the costs of greenhouse products, and they have also compiled the diagrams describing electricity consumption in case of traditional and non-traditional patterns of power supply; they also provide a power distribution pattern typical for greenhouse businesses, as well as the structure and the principle of operation of a co-generation unit used by a greenhouse facility.Results and discussion: the co-authors highlight the strengths of co-generation units used by greenhouse facilities. They have also identified the biological features of carbon dioxide generation and consumption, and they have listed the consequences of using carbon dioxide to enrich vegetable crops.Conclusion: the co-authors have formulated the expediency of using co-generation power plants as part of power generation facilities that serve greenhouses.

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