ArticleSensors (Basel, Switzerland)2022
Improving the Efficiency of Multistep Short-Term Electricity Load Forecasting via R-CNN with ML-LSTM.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Carbon emissions of power transmission and transformation projects in the whole life cycle for smart sustainable energy systems.Scientific reports · 2024Article
- Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review.Sensors (Basel, Switzerland) · 2023Review
- An IoT Enable Anomaly Detection System for Smart City Surveillance.Sensors (Basel, Switzerland) · 2023Article
- Solar Power Prediction Using Dual Stream CNN-LSTM Architecture.Sensors (Basel, Switzerland) · 2023Article
- An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition.Biomolecules · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
Multistep power consumption forecasting is smart grid electricity management's most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. However, an efficient electricity load forecasting model is required for accurate electric power management in an intelligent grid, leading to customer financial benefits. In this article, we develop an innovative framework for short-term electricity load forecasting, which includes two significant phases: data cleaning and a Residual Convolutional Neural Network (R-CNN) with multilayered Long Short-Term Memory (ML-LSTM) architecture. Data preprocessing strategies are applied in the first phase over raw data. A deep R-CNN architecture is developed in the second phase to extract essential features from the refined electricity consumption data. The output of R-CNN layers is fed into the ML-LSTM network to learn the sequence information, and finally, fully connected layers are used for the forecasting. The proposed model is evaluated over residential IHEPC and commercial PJM datasets and extensively decreases the error rates compared to baseline models.
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Registered trials
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