Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data

Abera, Fikirte Zemene; Khedkar, Vijayshri

doi:10.1007/s11277-019-06845-6

Cited by 19 publications

(11 citation statements)

References 20 publications

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“…The recently proposed technique utilizes Clara bunching for gathering the entire dataset into three different gatherings relying upon their essential feeding value, which future used with arrangement models artificial neural network and support vector machine to gauge with apparatus devoured extra energy inside different timeframes [16]. In this study, a novel illustration of highlight significance investigation as applied to brilliant meter order for non-private structures.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy Demand Forecasting Using Fused Machine Learning Approaches

Ghazal¹,

Noreen²,

Said³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Energy Demand Forecasting Using Fused Machine Learning Approaches

Ghazal¹,

Noreen²,

Said³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…They obtained RMSE of 5.05 and MAE of 3.05. [17] a method that uses the CLARA clustering technique to group their dataset into three clusters based on the mean of the consumption values. Then, SVM and ANN classifiers are used to predict the appliance that consumes more energy.…”

Section: Related Workmentioning

confidence: 99%

Development of an Efficient Electricity Consumption Prediction Model using Machine Learning Techniques

Alraddadi¹,

Othman²

2022

IJACSA

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Electricity consumption has continued to go up rapidly to follow the rapid growth of the economy. Therefore, detecting anomalies in buildings' energy data is considered one of the most essential techniques to detect anomalous events in buildings. This paper aims to optimize the electricity consumption in households by forecasting the consumption of these households and, consequently, identifying the anomalies. Further, as the used dataset is huge and published publicly, many research used part of it based on their needs. In this paper, the dataset is grouped as daily consumption and monthly consumption to compare the network topologies of all other works that used the same dataset with the selected part. The proposed methodology will depend basically on long short-term memory (LSTM) because it is powerful, flexible, and can deal with complex multi-dimensional time-series data. The results of the model can accurately predict the future consumption of individual households in a daily or monthly consumption base, even if the household was not included in the original training set. The proposed daily model achieves Root Mean Square Error (RMSE) value of 0.362 and mean absolute error (MAE) of 19.7%, while the monthly model achieves an RMSE value of 0.376 and MAE of 17.8%. Our model got the lowest accuracy result when compared with other compared network topologies. The lowest RMSE achieved from other topologies is 0.37 and the lowest MAE is 18% where our model achieved RMSE of 0.362 and MAE of 17.8%. Further, the model can detect the anomalies efficiently in both daily electricity consumption data and monthly electricity consumption data. However, the daily electricity consumption readings are way better to detect anomalies than the monthly electricity consumption readings because of the different picks that appear in the daily consumption data.

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“…e second type is artificial intelligence technology represented by neural network and its improved combination method. is method has good overall prediction performance but does not consider the characteristics of different time periods and different load types, and its efficiency needs to be improved when dealing with huge data volume and complex data structure [10].…”

Section: Introductionmentioning

confidence: 99%

Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

Zhang

Guo

Feng

et al. 2021

Scientific Programming

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For the problems of low accuracy and low efficiency of most load forecasting methods, a load forecasting method based on improved deep learning in cloud computing environment is proposed. Firstly, the preprocessed data set is divided into several data partitions with relatively balanced data volume through spatial grid, so as to better detect abnormal data. Then, the density peak clustering algorithm based on spark is used to detect abnormal data in each partition, and the local clusters and abnormal points are merged. The parallel processing of data is realized by using spark cluster computing platform. Finally, the deep belief network is used for load classification, and the classification results are input into the empirical mode decomposition-gating recurrent unit network model, and the load prediction results are obtained through learning. Based on the load data of a power grid, the experimental results demonstrate that the mean prediction error of the proposed method is basically controlled within 3% in the short term and 0.023 MW, 19.75%, and 2.76% in the long term, which are better than other comparison methods, and the parallel performance is good, which has a certain feasibility.

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Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data

Cited by 19 publications

References 20 publications

Energy Demand Forecasting Using Fused Machine Learning Approaches

Energy Demand Forecasting Using Fused Machine Learning Approaches

Development of an Efficient Electricity Consumption Prediction Model using Machine Learning Techniques

Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

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