Classification of Heritage Residential Building Stock and Defining Sustainable Retrofitting Scenarios in Khedivial Cairo

Abstract

This study aims to develop an integrated classification methodology for retrofitting that preserves both energy use and cultural value aspects in hot climates, especially, in North Africa, as a hot zone, which lacks retrofitting initiatives of built heritage. Despite the number of existing methods of classification for energy purposes, little attention has been paid to integrate the perceptions of cultural values in those methods. The proposed methodology classifies heritage building stocks based on building physical characteristics, as well as heritage significance levels, and then later integrates the outcomes into a matrix to propose sustainable retrofitting scenarios based on three dimensions, i.e., heritage value locations, types, and heritage significance level. For validation, the methodology was applied to the heritage residential building stock along with a microscale analysis on a building in Khedivial Cairo, Egypt. The findings include extracting twelve building classes, providing a reference building for each class, and a detailed catalogue of the extracted reference buildings that includes retrofitting scenarios for creating energy models. The originality of this work lies in integrating cultural values in a building classification methodology and providing a list of sustainable retrofitting scenarios for reference buildings. The findings contribute to fill the gap in existing building classifications, more specifically in hot climates.

Towards Nearly-Zero Energy in Heritage Residential Buildings Retrofitting in Hot, Dry Climates

“…These buildings are located in downtown Cairo, in the part well known as Khedivial Cairo. This part acts as a 'buffer zone' for the UNESCO Heritage Site of 'Historic Cairo' [12,24].…”

“…Both average values of floor area and building volume in different building typologies were considered. Therefore, based on our most recent publication, entitled "Classification of heritage residential building stock and defining sustainable retrofitting scenarios in Khedivial Cairo", heritage residential buildings in Khedivial Cairo were classified into twelve classes that represent twelve reference buildings [24]. The classification was based on the number of floors, number of adjoining walls, building construction type, and materials.…”

“…This article contributes to fill the gap in this field. Thus, this work is a part of a wider-scale research project comprising a series of published articles [12,24,25]. The research process has multiple stages that start with developing a classification method to select reference buildings of a heritage building stock [24], proposing a retrofitting checklist based on in-depth analysis of cultural values [24,25], and assessing the potential of applying passive retrofitting scenarios to enhance indoor thermal comfort [12].…”

“…Thus, this work is a part of a wider-scale research project comprising a series of published articles [12,24,25]. The research process has multiple stages that start with developing a classification method to select reference buildings of a heritage building stock [24], proposing a retrofitting checklist based on in-depth analysis of cultural values [24,25], and assessing the potential of applying passive retrofitting scenarios to enhance indoor thermal comfort [12]. Lastly, in this article, we assess the potential of transforming the heritage residential building stock towards "nearly-zero energy" and apply the entire methodology developed through the previous stages.…”

Stock Trend Prediction Algorithm Based on Deep Recurrent Neural Network

“…Secondly, on top of the original LSTM, this model is constructed by a mixture of Bi LSTM [17] and CLSTM, with Bi LSTM extracting stock trading data and investor sentiment index-related features and CLSTM integrating and processing the contextual features of the news, and finally outputting the prediction results through the fully connected layer. In the experimental model, the stock trend is experimented with using the classification method [18][19][20], and the classification is obtained as the probability of the stock going up and the probability of the stock going down. The experiments use the CSI 300 stock data as the data set.…”