Light-weight privacy enabled topology establishment and communication protocol for swarm IoT networks

Sheena, N.; Joseph, Shelbi; Sivan, Shailesh; Bhushan, Bharat

doi:10.1007/s10586-022-03815-9

Cited by 2 publications

(2 citation statements)

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“…where ŷ is the forecasted variable, t is the number of trees, and p( ŷl |x) is the probability function of the forecasted variable given the independent variables. Thus, because of the dependence of the decision trees, the objective function J, in addition to seeking to minimize the error, considers the complexity of the set of decision trees, as shown in (4).…”

Section: Random Forestmentioning

confidence: 99%

“…This transition has a large impact on the building sector, where an increase in electric energy is expected [1,2]. The increase in demand is mainly generated by the conversion of thermal to electrical consumption through HVAC systems, and there is a worldwide trend for interconnection of essential appliances by means of the Internet, forming the IoT [3,4]. Electrical vehicles also have a significant influence on the increase in demand due to their need for charging points in buildings [5,6].…”

Section: Introductionmentioning

confidence: 99%

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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: Random Forestmentioning

confidence: 99%