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In this paper, we present an investigation of the possibility to use a neural network combined with a quasi-physical description in order to predict the annual ...
In the present study, simple polynomial function is used to estimate the annual energy demand as a function of building envelope physical and geometric ...
This article presents a comparative analysis of two prominent machine learning techniques for predicting electricity consumption in workplace lighting systems.
By using the 4th polynomial function that have two double radix and a feature the f(x) = a4 in x = 0 condition, we can calculate annual building load very ...
Nov 10, 2020 ¡¤ We propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently.
In this paper, we review the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, ...
To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction ...
Jan 19, 2020 ¡¤ This study targets a simple and low-cost load forecasting method for office buildings in South Korea with four distinct seasons. Background.
This research investigates various ML methods for predicting energy efficiency in buildings, with a particular emphasis on heating and cooling loads.
Oct 2, 2018 ¡¤ This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions ...