Smart Building Energy Management: Load Profile Prediction using Machine Learning

Revati, G.; Hozefa, J.; Shadab, S.; Sheikh, A.; Wagh, Sushama; Singh, Navdeep

doi:10.1109/med51440.2021.9480170

Cited by 20 publications

(3 citation statements)

References 14 publications

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“…If there are nonlinear correlations in the data, the linear regression model performs poorly. Revati et al [17] suggested employing Gaussian process regression to anticipate consumer behavior to focus on a data-driven approach to load profile prediction with the emphasized benefit of a model-free environment and useful for setting up a specific demand response plan to receive incentives like money. This modal cannot locate the grouped data.…”

Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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Consumption behavior prediction reveals customer attributes, personal preferences, and intrinsic laws. Organizations would benefit from knowing further about customer needs and business desires by monitoring client behavior to provide more precise recommendations and boost acquisition rates. The economics of the customer, buyer groupings, and product quality are only a few of the numerous variables that influence customer behavior. The key issue that has to be resolved at this time is how to filter out useful information from these vast amounts of data to forecast customer behavior. For customer consumption behavior prediction and analysis with an advanced quantitative research process, we proposed the multiobjective evolutionary algorithm, which significantly boosts the accuracy of consumption behavior predictions. The dataset is initially gathered based on consumer preferences and behaviors as the essential information for the entire prediction model. Min-max normalization is used as a component of the preprocessing of the data to get the elimination of redundant and superfluous data. The Word2vec model is utilized for feature extraction, and boosted ant colony optimization (BACO) is employed to choose the best features. Utilizing the suggested multiobjective evolutionary algorithm (MOEA), the predictions are made. The suggested system’s performance is assessed, and the metrics are contrasted with more established methods. The findings demonstrate that the suggested MOEA technique performs well than the traditional ML, XGB, AI, and HNB algorithm methods in terms of accuracy (95 percent), quality of prediction (97 percent), precision (99 percent), recall (93 percent), F 1 -score (98 percent), and prediction time (50 seconds). Hence, the outcomes show that the regression model is sustainable. The suggested consumption behavior prediction system has demonstrated its efficiency in boosting profitability.

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Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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“…Electricity consumed by the HVAC and refrigeration systems of one supermarket is predicted using ANN [37]. ANN, Gaussian process regression, linear regression and dynamic mode decomposition are compared in the prediction of 1-h weekday profiles of a commercial building [38]. Lastly, deep learning models (large neural-networks) have been also explored for this problem, however they need large data-sets to estimate the model parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

A reduced-dimension feature extraction method to represent retail store electricity profiles

Granell

Axon

Kolokotroni

et al. 2022

Energy and Buildings

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“…Despite this, residential users lack the motivation to apply energy saving and carbon reduction measures and introduce home energy management systems (HEMS) due to the low electric valance in Taiwan. Therefore, some research [1][2][3][4][5][6][7][8][9][10][11][12][13] has begun using machine learning techniques to collect and analyze the electricity consumption data of residential users and establish artificial intelligence (AI) models to provide appropriate and tailored energy-saving suggestions. Among them, if a mechanism can identify the abnormal electricity consumption behavior of residential users and propose appropriate energy management or saving suggestions, it will be particularly effective in improving user motivation in terms of energy-saving measures.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder

et al. 2022

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Due to the climate crisis, energy-saving issues and carbon reduction have become the top priority for all countries. Owing to the increasing popularity of advanced metering infrastructure and smart meters, the cost of acquiring data on residential electricity consumption has substantially dropped. This change promotes the analysis of residential electricity consumption, which features both small and complicated consumption behaviors, using machine learning to become an important research topic among various energy saving and carbon reduction measures. The main subtopic of this subject is the identification of abnormal electricity consumption behaviors. At present, anomaly detection is typically realized using models based on low-level features directly collected by sensors and electricity meters. However, due to the significant number of dimensions and a large amount of redundant information in these low-level features, the training efficiency of the model is often low. To overcome this, this study adopts an autoencoder, which is a deep learning technology, to extract the high-level electricity consumption information of residential users to improve the anomaly detection performance of the model. Subsequently, this study trains one-class SVM models for anomaly detection by using the high-level features of five actual residential users to verify the benefits of high-level features.

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Smart Building Energy Management: Load Profile Prediction using Machine Learning

Cited by 20 publications

References 14 publications

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

A reduced-dimension feature extraction method to represent retail store electricity profiles

Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder

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