Joint Household Characteristic Prediction via Smart Meter Data

Sun, Gan; Cong, Yang; Hou, Dongdong; Fan, Huijie; Xu, Xiaowei; Yu, Haibin

doi:10.1109/tsg.2017.2778428

Cited by 34 publications

(12 citation statements)

References 25 publications

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“…These data had five attributes, and a K-means clustering technique similar to the one proposed in this study was used. In [ 28 ], the authors worked with 1.5 years of data containing 81 attributes. A data normalization step was included to pre-process the data.…”

Section: Results and Simulation For Models For Time Series Forecastingmentioning

confidence: 99%

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Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat

Qadir

et al. 2021

Sensors

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Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

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Section: Results and Simulation For Models For Time Series Forecastingmentioning

confidence: 99%

“…A data normalization step was included to pre-process the data. This involved applying the zero mean and unit variance-based method on the data [ 28 ]. In study [ 29 ], 29 features were included in the dataset.…”

Section: Results and Simulation For Models For Time Series Forecastingmentioning

confidence: 99%

Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat

Qadir

et al. 2021

Sensors

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“…Recent advances in artificial intelligence (AI) have led to the emergence of new intelligent automatic systems. AI, nowadays, acts as a bridge between different fields such as economics, biology, physics, mathematics, chemistry, etc [1]- [8]. AI methods can be applied to solve a variety of problems existing in those fields, providing more accurate solutions than standard classification methods.…”

Section: Introductionmentioning

confidence: 99%

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

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Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques. INDEX TERMS Coronary artery disease (CAD), machine learning, ensemble learning, nested ensemble (NE) model, genetic algorithm.

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“…The effective management and analysis of AMI data can facilitate a bidirectional information flow and friendly interaction between customers and grids [2]. They also play a nontrivial role in accurate demand response (DR) [3], power reliability and efficiency improvement [4], electricity price design [5], and other personalized services [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding

Liu

Sun

2019

Information

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The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise demand response. These services basically rely on the accurate estimation of electricity demand, and the key challenge lies in the high volatility and uncertainty of load profiles and the tremendous communication pressure on the data link or computing center. This study proposed a novel two-stage approach for estimating household electricity demand based on edge deep sparse coding. In the first sparse coding stage, the status of electrical devices was introduced into the deep non-negative k-means-singular value decomposition (K-SVD) sparse algorithm to estimate the behavior of customers. The patterns extracted in the first stage were used to train the long short-term memory (LSTM) network and forecast household electricity demand in the subsequent 30 min. The developed method was implemented on the Python platform and tested on AMPds dataset. The proposed method outperformed the multi-layer perception (MLP) by 51.26%, the autoregressive integrated moving average model (ARIMA) by 36.62%, and LSTM with shallow K-SVD by 16.4% in terms of mean absolute percent error (MAPE). In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD. The results of the experiments demonstrated that the proposed method can provide considerable and stable improvement in household electricity demand estimation.

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Joint Household Characteristic Prediction via Smart Meter Data

Cited by 34 publications

References 25 publications

Cluster Analysis and Model Comparison Using Smart Meter Data

Cluster Analysis and Model Comparison Using Smart Meter Data

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding

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