Study on the Use of Artificially Generated Objects in the Process of Training MLP Neural Networks Based on Dispersed Data

Marfo, Kwabena Frimpong; Przybyła–Kasperek, Małgorzata

doi:10.3390/e25050703

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…Furthermore, if a node has more than one input, the final value of the function of this node corresponds to the sum of the individual values of the functions of the node and their connections. The end of each step performed in the iterative training process is terminated by backward propagation, where the error is propagated to the input layers, and updating the values of the weights [65,66]. As in MLs, learning is based on minimizing a cost function J.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

Load Forecasting with Machine Learning and Deep Learning Methods

et al. 2023

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Characterizing the electric energy curve can improve the energy efficiency of existing buildings without any structural change and is the basis for controlling and optimizing building performance. Artificial Intelligence (AI) techniques show much potential due to their accuracy and malleability in the field of pattern recognition, and using these models it is possible to adjust the building services in real time. Thus, the objective of this paper is to determine the AI technique that best forecasts electrical loads. The suggested techniques are random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), long short-term memory (LSTM), and temporal convolutional network (Conv-1D). The conducted research applies a methodology that considers the bias and variance of the models, enhancing the robustness of the most suitable AI techniques for modeling and forecasting the electricity consumption in buildings. These techniques are evaluated in a single-family dwelling located in the United States. The performance comparison is obtained by analyzing their bias and variance by using a 10-fold cross-validation technique. By means of the evaluation of the models in different sets, i.e., validation and test sets, their capacity to reproduce the results and the ability to properly forecast on future occasions is also evaluated. The results show that the model with less dispersion, both in the validation set and test set, is LSTM. It presents errors of −0.02% of nMBE and 2.76% of nRMSE in the validation set and −0.54% of nMBE and 4.74% of nRMSE in the test set.

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Section: Multilayer Perceptronmentioning

confidence: 99%