Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

Accurate pressure drop estimation in forced boiling phenomena is important during the thermal analysis and the geometric design of cryogenic heat exchangers. However, current methods to predict the pressure drop have one of two problems: lack of accuracy or generalization to different situations. In this work, we present the correlated-informed neural networks (CoINN), a new paradigm in applying the artificial neural network (ANN) technique combined with a successful pressure drop correlation as a mapping tool to predict the pressure drop of zeotropic mixtures in micro-channels. The proposed approach is inspired by Transfer Learning, highly used in deep learning problems with reduced datasets. Our method improves the ANN performance by transferring the knowledge of the Sun & Mishima correlation for the pressure drop to the ANN. The correlation having physical and phenomenological implications for the pressure drop in micro-channels considerably improves the performance and generalization capabilities of the ANN. The final architecture consists of three inputs: the mixture vapor quality, the micro-channel inner diameter, and the available pressure drop correlation. The results show the benefits gained using the correlated-informed approach predicting experimental data used for training and a posterior test with a mean relative error (mre) of 6%, lower than the Sun & Mishima correlation of 13%. Additionally, this approach can be extended to other mixtures and experimental settings, a missing feature in other approaches for mapping correlations using ANNs for heat transfer applications

Development of electricity consumption profiles of residential buildings based on smart meter data clustering

In the present research, a high-resolution, detailed electric load dataset was assessed, collected by smart meters from nearly a thousand households in Hungary, many of them single-family houses. The objective was to evaluate this database in detail to determine energy consumption profiles from time series of daily and annual electric load. After representativity check of dataset daily and annual energy consumption profiles were developed, applying three different clustering methods (k-means, fuzzy k-means, agglomerative hierarchical) and three different cluster validity indexes (elbow method, silhouette method, Dunn index) in MATLAB environment. The best clustering method for our examination proved to be the kmeans clustering technique. Analyses were carried out to identify different consumer groups, as well as to clarify the impact of specific parameters such as meter type in the housing unit (e.g. peak, offpeak meter), day of the week (e.g. weekend, weekday), seasonality, geographical location, settlement type and housing type (single-family house, flat, age class of the building). Furthermore, four electric user profile types were proposed, which can be used for building energy demand simulation, summer heat load and winter heating demand calculatio