The Community Renewal of Shantytown Transformation in Old Industrial Cities:Evidence from Tiexi Worker Village in Shenyang,China

There are many problems,such as poverty,unemployment,poor infrastructure and how to improve the ecological environment,in the shantytowns of old industrial cities.Shantytowns are the most centralized areas with different contradictions,referred to as the‘problem areas’of urban sustainable development.The shantytown transformation of old industrial cities is a typical process of unit community disintegration,which is reflected not only in the reconstruction of physical space but also in the community renewal of social spatial integration and culture reconstruction.Based on qualitative research,questionnaires and in-depth interviews,taking Tiexi Worker Village in Shenyang as a research case,this paper attempts to analyse the characteristics and driving forces of the community renewal of shantytown transformation in old industrial cities.We found that the physical space of Tiexi Worker Village has changed considerably,which is embodied in its land use structure,living environment and community service facilities,reflecting the development of community function from simple industrial function to comprehensive functions.The residents in this community have experienced a transformation from a period of homogeneity to one of heterogeneity.The social network of the community has been destroyed.Social stratification,social differentiation and higher fluidity have occurred.Community renewal is mainly affected by macro factors such as policy regulation,economic driving,condition constraints,and micro factors such as residents’choice of living space and willingness to renew their communities.The transformation policy of the old industrial zone and the development policy of the new urban area are the fundamental and deep-seated reasons for the renewal of Tiexi Worker Village,which determine the direction of the renewal and development of the community.The paid use of land and the development of the real estate industry are the direct stimulating factors for the renewal of Tiexi Worker Village,which become the direct reasons for the spatial transformation.The changes in the population in urban and rural areas and the promotion of traffic are the driving factors for the renewal of the community,while the choice of residents and their willingness regarding community renewal are the endogenous forces for promoting community renewal.Finally,the author attempts to put forward a model of the interlaced mechanism of the forming of community renewal at the macroscopic and microcosmic levels,which are the urban renewal and reconstruction and social space differentiation of community residents,respectively.

Community Renewal Strategy for Improving Community Resilience:A Case Study of River Ring Area of Caoyang New Village in Shanghai

In a new round of urban renewal,China’s urban space expansion is shifting from incremental development to inventory mining.Residents’ demands for improvement of community material environment and community cultural identity are increasing.Meantime,affected by the COVID-19 pandemic in 2019,community resilience has become an urgent problem to be solved in urban communities.Based on resilience theory,TOD theory and other planning concepts,the paper analyzes the vulnerability of Shanghai Caoyang river ring area,and puts forward a multi-level community resilience improvement strategy,including increasing diversified public service facilities,building public pedestrian network,and reshaping public open space to improve the stability,adaptability and resilience of the community,in order to build the development path of resilient communities.The study will provide inspiration for future micro-renewal of communities and promote the sustainable development of urban communities.

Promoting Functional Upgrading Through Spatial Renovation: An Exploration on the Design Methods of Old Community Renewal

As urban renewal shifts to more routine community renewal, the design methods of community renewal are faced with new challenges. Considering the existing dilemma faced by old communities and based on the practice of community renewal of different scales in recent years, this paper proposes that the design for community renewal should consider not only the spatial structure of communities at the macro level, but also the needs of daily life at the micro level. It can help efficiently transform the existing space into public space that encourages residents’ participation and promote community vitality, to achieve a humanistic value beyond the physical environment.

IRENO-ARCHITECTURE:A Possible Adaptation of Architecture for Peace in Honduras

Young people living inside informal communities around the capital of Honduras are easily recruited by gangs.These neighborhoods demand better quality architecture that is able to play a role as promoter of peace.In order to provide solid bases for new architectural solutions,data from various resources were used to perform several analysis and studies,in order to develop and exemplify design criteria for architecture for peace.In this way,it is possible to find a target audience to whom social architecture,in the specific context of Honduras should be aimed,and provide a starting point for future investigations and management on the subject.

Reconstructing hourly residential electrical load profiles for Renewable Energy Communities using non-intrusive machine learning techniques

The successful implementation of Renewable Energy Communities(RECs)involves maximizing the self-consumption within a community,particularly in regulatory contexts in which shared energy is incentivized.In many countries,the absence of a metering infrastructure that provides data at an hourly or sub-hourly resolution level for low-voltage users(e.g.,residential and commercial users)makes the design of a new energy community a challenging task.This study proposes a non-intrusive machine learning methodology that can be used to generate residential electrical consumption profiles at an hourly resolution level using only monthly consumption data(i.e.,billed energy),with the aim of estimating the energy shared by RECs.The proposed methodology involves three phases:first,identifying the typical load patterns of residential users through k-Means clustering,then implementing a Random Forest algorithm,based on monthly energy bills,to identify typical load patterns and,finally,reconstructing the hourly electrical load profile through a data-driven rescaling procedure.The effectiveness of the proposed methodology has been evaluated through an REC case study composed by 37 residential users powered by a 70 kWp photovoltaic plant.The Normalized Mean Absolute Error(NMAE)and the Normalized Root Mean Squared Error(NRMSE)were evaluated over an entire year and whenever the energy was shared within the REC.The Relative Absolute Error was also measured when estimating the shared energy at both a monthly(MRAE)and at an annual basis.(RAE).A comparison between the REC load profile reconstructed using the proposed methodology and the real load profile yielded an overall NMAE of 20.04%,an NRMSE of 26.17%,and errors of 18.34%and 23.87%during shared energy timeframes,respectively.Furthermore,our model delivered relative absolute errors for the estimation of the shared energy at a monthly and annual scale of 8.31%and 0.12%,respectively.

A new hybrid AI optimal management method for renewable energycommunities

In this study, we propose a hybrid AI optimal method to improve the efficiency of energy managementin a smart grid such as Renewable Energy Community. This method adopts a Time Delay Neural Networkto forecast the future values of the energy features in the community. Then, these forecasts are used by astochastic Model Predictive Control to optimize the community operations with a proper control strategy ofBattery Energy Storage System. The results of the predictions performed on a public dataset with a predictionhorizon of 24 h return a Mean Absolute Error of 1.60 kW, 2.15 kW, and 0.30 kW for photovoltaic generation,total energy consumption, and common services, respectively. The model predictive control fed with suchpredictions generates maximum income compared to the competitors. The total income is increased by 18.72%compared to utilizing the same management system without exploiting predictions from a forecasting method.