Improving load forecast accuracy by clustering consumers using smart meter data

Shahzadeh, Abbas; Khosravi, Abbas; Nahavandi, Saeid

doi:10.1109/ijcnn.2015.7280393

Cited by 46 publications

(19 citation statements)

References 7 publications

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“…Various studies have looked at classification of building with various objectives using temporal meter data as a source of features [19][20][21]16,22]. Several other studies have extracted temporal features that enhance the ability to forecast consumption [23][24][25]. Several studies have analyzed larger than usual datasets from devices such as water heaters [26] and retrofit analysis at the city scale [27].…”

Section: Previous Workmentioning

confidence: 99%

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Miller

Meggers

2017

Energy and Buildings

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This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6% more accurate for primary building use type, 24.3% more accurate for performance class, and 63.6% more accurate for building operations type as compared to baselines.

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Section: Previous Workmentioning

confidence: 99%

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Miller

Meggers

2017

Energy and Buildings

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“…These fluctuations are often unpredictable due to the dynamic nature of the behaviour of the household residents. Furthermore, there are studies that show that the hybridisation of clustering with forecasting techniques improves prediction performance [15,16]. Recent works on training artificial neural networks (ANNs) with residential SM data for energy consumption predictions can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis

2021

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This study presents a novel approach for discovering actionable knowledge and exploring data-based models from data recorded by household smart meters. The proposed framework is supported by a machine learning architecture based on the application of data mining methods and spatial analysis to extract temporal and spatial restricted clusters of characteristic monthly electricity load profiles. In addition, it uses these clusters to perform short-term load forecasting (1 week) using recurrent neural networks. The approach analyses a database with measurements of 1000 smart meters gathered during 4 years in Guayaquil, Ecuador. Results of the proposed methodology led us to obtain a precise and efficient stratification of typical consumption patterns and to extract neighbour information to improve the performance of residential energy consumption forecasting.

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“…This aspect is used to group the patterns of batteries within the data center and improve the forecasting model instead of predicting thousands of batteries individually. Clustering algorithms, like Dynamic Time Warping (DTW), hierarchical, fuzzy, k-shape, and TADPole all have unique functionality for grouping similar data points, and the features selected by clustering improve the model forecasting accuracy [28][29][30]. The proposed cluster-assisted forecasting results are compared with actual battery data and without clustered ARIMA forecasting.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Prediction for Batteries in Data Centers

Haider

Zhao

2020

Energies

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Prediction of a battery's health in data centers plays a significant role in Battery Management Systems (BMS). Data centers use thousands of batteries, and their lifespan ultimately decreases over time. Predicting battery's degradation status is very critical, even before the first failure is encountered during its discharge cycle, which also turns out to be a very difficult task in real life. Therefore, a framework to improve Auto-Regressive Integrated Moving Average (ARIMA) accuracy for forecasting battery's health with clustered predictors is proposed. Clustering approaches, such as Dynamic Time Warping (DTW) or k-shape-based, are beneficial to find patterns in data sets with multiple time series. The aspect of large number of batteries in a data center is used to cluster the voltage patterns, which are further utilized to improve the accuracy of the ARIMA model. Our proposed work shows that the forecasting accuracy of the ARIMA model is significantly improved by applying the results of the clustered predictor for batteries in a real data center. This paper presents the actual historical data of 40 batteries of the large-scale data center for one whole year to validate the effectiveness of the proposed methodology.

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Improving load forecast accuracy by clustering consumers using smart meter data

Cited by 46 publications

References 7 publications

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis

Cluster-Based Prediction for Batteries in Data Centers

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