ArticleSensors (Basel, Switzerland)2021
Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble.
Article in Sensors (Basel, Switzerland), 2021. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Deep learning framework for predicting measurement error drift in smart meter sensors under harsh coastal environments.PloS one · 2026Article
- EEG dataset for energy data visualizations.Data in brief · 2024Article
- An electricity smart meter dataset of Spanish households: insights into consumption patterns.Scientific data · 2024Article
- Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives.European journal of medical research · 2023Review
- A Machine Learning Model Ensemble for Mixed Power Load Forecasting across Multiple Time Horizons.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors.
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Abstract
The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.
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Registered trials
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