scholarly journals Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8096
Author(s):  
Paulo S. G. de Mattos Neto ◽  
João F. L. de Oliveira ◽  
Priscilla Bassetto ◽  
Hugo Valadares Siqueira ◽  
Luciano Barbosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Performance Metrics ◽  
Extreme Learning Machines ◽  
Statistical Machine Learning ◽  
Smart Meters ◽  
Learning Machines ◽  
Echo State Networks ◽  
Artificial Neural

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.

Unorganized Machines

2014 ◽  
pp. 221-236 ◽  
Author(s):  
Levy Boccato ◽  
Everton S. Soares ◽  
Marcos M. L. P. Fernandes ◽  
Diogo C. Soriano ◽  
Romis Attux
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Computing ◽  
State Machines ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Alan Turing ◽  
Echo State Networks ◽  
The Relationship ◽  
And Training

This work presents a discussion about the relationship between the contributions of Alan Turing – the centenary of whose birth is celebrated in 2012 – to the field of artificial neural networks and modern unorganized machines: reservoir computing (RC) approaches and extreme learning machines (ELMs). Firstly, the authors review Turing’s connectionist proposals and also expose the fundamentals of the main RC paradigms – echo state networks and liquid state machines, - as well as of the design and training of ELMs. Throughout this exposition, the main points of contact between Turing’s ideas and these modern perspectives are outlined, being, then, duly summarized in the second and final part of the work. This paper is useful in offering a distinct appreciation of Turing’s pioneering contributions to the field of neural networks and also in indicating some perspectives for the future development of the field that may arise from the synergy between these views.

Unorganized Machines

2011 ◽  
Vol 2 (4) ◽  
pp. 1-16 ◽  
Author(s):  
Levy Boccato ◽  
Everton S. Soares ◽  
Marcos M. L. P. Fernandes ◽  
Diogo C. Soriano ◽  
Romis Attux
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reservoir Computing ◽  
State Machines ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Alan Turing ◽  
Echo State Networks ◽  
The Relationship ◽  
And Training

This work presents a discussion about the relationship between the contributions of Alan Turing – the centenary of whose birth is celebrated in 2012 – to the field of artificial neural networks and modern unorganized machines: reservoir computing (RC) approaches and extreme learning machines (ELMs). Firstly, the authors review Turing’s connectionist proposals and also expose the fundamentals of the main RC paradigms – echo state networks and liquid state machines, - as well as of the design and training of ELMs. Throughout this exposition, the main points of contact between Turing’s ideas and these modern perspectives are outlined, being, then, duly summarized in the second and final part of the work. This paper is useful in offering a distinct appreciation of Turing’s pioneering contributions to the field of neural networks and also in indicating some perspectives for the future development of the field that may arise from the synergy between these views.

scholarly journals Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling

2013 ◽  
Vol 17 (1) ◽  
pp. 253-267 ◽  
Author(s):  
N. J. de Vos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Internal State ◽  
Relevant Information ◽  
Training Procedure ◽  
Recurrent Networks ◽  
Eastern United States ◽  
Rainfall Runoff ◽  
Echo State Networks ◽  
Artificial Neural

Abstract. Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses the recently introduced and conceptually simple echo state networks for streamflow forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent approaches. Two modifications on the echo state network models are made that increase the hydrologically relevant information content of their internal state. The results show that the echo state networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that they can be considered promising alternatives to traditional artificial neural networks in rainfall–runoff modelling.

Building Energy Prediction Using Artificial Neural Networks (LSTM)

2020 ◽  
Author(s):  
Sankhanil Goswami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Prediction Models ◽  
Short Term Memory ◽  
Significant Proportion ◽  
Cooling Load ◽  
Energy Prediction ◽  
Artificial Neural

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.

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