Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach

Lascio, F. Marta L. Di; Menapace, Andrea; Righetti, Maurizio

doi:10.1007/s10260-019-00488-4

Cited by 11 publications

(10 citation statements)

References 56 publications

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Section: Literature Reviewmentioning

confidence: 99%

“…Although copulas are used in many areas, the introduction to CoClust is with Di Lascio [21]. The study that introduced the CoClust technique into the field is a doctoral dissertation that develops the technique further on clinical microarray data analysis [22], Di Lascio [21]. In 2017, the development of forty European countries was examined by healthy nutrition rules with CoClust, and later in 2019, they examined the improved version of the technique (Di Lascio, Durante, and Pappada [23]; Di Lascio and Giannerini [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…CoClust was introduced by Di Lascio in [21] through her doctoral thesis, developed in 2017, and the final version of the technique was presented by Di Lascio and Giannerini [24].…”

Section: Coclustmentioning

confidence: 99%

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An enhanced random forest approach using CoClust clustering: MIMIC-III and SMS spam collection application

2023

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The random forest algorithm could be enhanced and produce better results with a well-designed and organized feature selection phase. The dependency structure between the variables is considered to be the most important criterion behind selecting the variables to be used in the algorithm during the feature selection phase. As the dependency structure is mostly nonlinear, making use of a tool that considers nonlinearity would be a more beneficial approach. Copula-Based Clustering technique (CoClust) clusters variables with copulas according to nonlinear dependency. We show that it is possible to achieve a remarkable improvement in CPU times and accuracy by adding the CoClust-based feature selection step to the random forest technique. We work with two different large datasets, namely, the MIMIC-III Sepsis Dataset and the SMS Spam Collection Dataset. The first dataset is large in terms of rows referring to individual IDs, while the latter is an example of longer column length data with many variables to be considered. In the proposed approach, first, random forest is employed without adding the CoClust step. Then, random forest is repeated in the clusters obtained with CoClust. The obtained results are compared in terms of CPU time, accuracy and ROC (receiver operating characteristic) curve. CoClust clustering results are compared with K-means and hierarchical clustering techniques. The Random Forest, Gradient Boosting and Logistic Regression results obtained with these clusters and the success of RF and CoClust working together are examined.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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An enhanced random forest approach using CoClust clustering: MIMIC-III and SMS spam collection application

2023

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“…Several methods have been proposed for modelling this relationship, such as multi regression models [37], autoregressive and moving average models [38], copula-based models [39,40], and machine learning techniques including neural network and support vector machine [41].…”

Section: Seasonal Dependence Modelling Between Heat Demand and Outside Temperaturementioning

confidence: 99%

Stochastic Generation of District Heat Load

et al. 2021

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Modelling heat load is a crucial challenge for the proper management of heat production and distribution. Several studies have tackled this issue at building and urban levels, however, the current scale of interest is shifting to the district level due to the new paradigm of the smart system. This study presents a stochastic procedure to model district heat load with a different number of buildings aggregation. The proposed method is based on a superimposition approach by analysing the seasonal component using a linear regression model on the outdoor temperature and the intra-daily component through a bi-parametric distribution of different times of the day. Moreover, an empirical relationship, that estimates the demand variation given the average demand together with a user aggregation coefficient, is proposed. To assess the effectiveness of the proposed methodology, the study of a group of residential users connected to the district heating system of Bozen-Bolzano is carried out. In addition, an application on a three-day prevision shows the suitability of this approach. The final purpose is to provide a flexible tool for district heat load characterisation and prevision based on a sample of time series data and summary information about the buildings belonging to the analysed district.

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“…We, then, analyze panel data of DH demand in an Italian city. While some studies exploit copulas in the field of DH demand (see, e.g., [31], [32]), copulabased clustering has not yet been developed -or only marginally -in relation to energy or the more general environmental sciences field (see, e.g., [33], [34]).…”

Section: Introductionmentioning

confidence: 99%

A Spatial AMH Copula-Based Dissimilarity Measure to Cluster Variables in Panel Data

Lascio

Menapace

Pappadà

2021

Preprint

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Investigating thermal energy demand is crucial for the development of sustainable cities and efficient use of renewable sources. Despite the advances made in this field, the analysis of energy data provided by smart grids is currently a demanding challenge. In this paper, we develop a clustering methodology based on a novel dissimilarity measure to analyze a high temporal resolution panel data for district heating demand in the Italian city Bozen-Bolzano. Starting from the characteristics of this data, we explore the usefulness of the Ali-Mikhail-Haq copula in defining a new dissimilarity measure to cluster variables in a hierarchical framework. We show that our proposal is particularly sensitive to small dissimilarities based on tiny differences in the dependence level. Therefore, the proposed measure is able to better distinguish between objects with low dissimilarity than classic rank-based dissimilarity measures. Moreover, our proposal is defined in a spatial version that is able to take into account the spatial location of the compared objects. We investigate the proposed measure through Monte Carlo studies and compare it with the corresponding spatial Kendall's correlation-based dissimilarity measure. Finally, the application to real data makes it possible to find clusters of buildings homogeneous with respect to their main characteristics, such as energy efficiency and heating surface, to support the design, expansion and management of district heating systems.

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Joint and conditional dependence modelling of peak district heating demand and outdoor temperature: a copula-based approach

Cited by 11 publications

References 56 publications

An enhanced random forest approach using CoClust clustering: MIMIC-III and SMS spam collection application

An enhanced random forest approach using CoClust clustering: MIMIC-III and SMS spam collection application

Stochastic Generation of District Heat Load

A Spatial AMH Copula-Based Dissimilarity Measure to Cluster Variables in Panel Data

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