Incremental mining of frequent power consumption patterns from smart meters big data

2016 ◽  
Author(s):  
Shailendra Singh ◽  
Abdulsalam Yassine ◽  
Shervin Shirmohammadi
Keyword(s):  
Big Data ◽  
Power Consumption ◽  
Consumption Patterns ◽  
Incremental Mining ◽  
Smart Meters

scholarly journals Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

2020 ◽  
Vol 12 (8) ◽  
pp. 3158 ◽  
Author(s):  
Arash Moradzadeh ◽  
Omid Sadeghian ◽  
Kazem Pourhossein ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Power Consumption ◽  
Sustainability Assessment ◽  
Dimensional Space ◽  
Principal Component ◽  
Energy System ◽  
Component Analysis ◽  
Consumption Patterns ◽  
Smart Meters

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

scholarly journals Analysis of Marketing Innovation Under the New Retail Mode-Taking “Luckin coffee” as an Example

2021 ◽  
Vol 235 ◽  
pp. 01074
Author(s):  
Siyu Wei
Keyword(s):  
Big Data ◽  
Operation Mode ◽  
Marketing Strategies ◽  
Consumption Patterns ◽  
Service Process ◽  
Strong Impact ◽  
Fully Integrated ◽  
Marketing Innovation ◽  
Data Prediction ◽  
Differentiated Marketing

With the rise of high-end technologies such as artificial intelligence and big data, many new consumption patterns are constantly emerging, from offline retail to online e-commerce and then to a New Retail mode combining online e-commerce, offline and logistics. The New Retail mode not only brings a strong impact to the traditional retail industry, but also provides many enterprises with new operation and marketing ideas. The research object selected by the author is Luckin coffee, a representative enterprise of the New Retail mode, by analyzing the operation mode of “online APP+ offline store + logistics” used by Luckin coffee and the marketing method implemented under this mode, the innovation of Luckin coffee’s marketing method is concluded, including Viral social fission marketing, Innovation of service process, Big data differentiated marketing, Novel scene positioning, Omni-channel retail, which can provide some ideas such as Social fission, Big data prediction, differentiated marketing, Fully integrated channels and so on for other traditional retail enterprises to reform and new Retail enterprises to formulate marketing strategies.

scholarly journals Analyzing and Predicting Power Consumption Profiles Using Big Data

2019 ◽  
pp. 392-401
Author(s):  
Amelec Viloria ◽  
Ronald Prieto Pulido ◽  
Jesús García Guiliany ◽  
Jairo Martínez Ventura ◽  
Hugo Hernández Palma ◽  
...  
Keyword(s):  
Big Data ◽  
Power Consumption

scholarly journals Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5236 ◽  
Author(s):  
Sanket Desai ◽  
Rabei Alhadad ◽  
Abdun Mahmood ◽  
Naveen Chilamkurti ◽  
Seungmin Rho
Keyword(s):  
Power Consumption ◽  
Large Scale ◽  
Single Point ◽  
Ground Truth ◽  
Significant Rise ◽  
The State ◽  
Smart Meters ◽  
Energy Estimation ◽  
Consumption Data ◽  
Load Monitoring

With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.

A Study on the Power Consumption Patterns of General Hospital Buildings

2015 ◽  
Vol 1092-1093 ◽  
pp. 573-577
Author(s):  
Myung Kil Yeo ◽  
Kang Guk Lee ◽  
Won Hwa Hong
Keyword(s):  
Power Consumption ◽  
General Hospital ◽  
Power Supply ◽  
Energy Use ◽  
Consumption Patterns ◽  
Basic Data ◽  
Energy Usage ◽  
General Hospitals ◽  
Usage Patterns ◽  
Save Energy

The most important part of energy use in hospital buildings is to identify its usage patterns and maintain power supply even in blackout situations, rather than to save energy. This paper presents the power energy usage patterns of general hospitals as basic data for the establishment of countermeasures in blackout situations.

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