Planning Tool Research Articles

Evaluating effectiveness of clustering algorithms in multiple target stereotactic radiosurgery

Objective. Single-isocenter-multiple-target technique for stereotactic radiosurgery (SRS) can reduce treatment duration but risks compromised dose coverage due to potential rotational errors. Clustering targets into two groups can reduce isocenter-target distances, mitigating the impact of rotational uncertainty. However, a comprehensive evaluation of clustering algorithms for SRS is absent. This study addresses this gap by introducing the SRS Target Clustering Framework (Framework), a comprehensive tool that utilizes commonly used clustering algorithms to generate efficient cluster configurations. Approach. The Framework incorporates four distinct optimization objectives based on two key metrics: the isocenter-target distance and the ratio of this distance to the target radius. Agglomerative and weighted agglomerative clustering are employed for minimax and weighted minimax objectives, respectively. K-means and weighted k-means are utilized for sum-of-squares and weighted sum-of-squares objectives. We applied the Framework to 126 SRS plans, comparing results to ground truth solutions obtained through a brute force algorithm. Main results. For the minimax objective, the average maximum isocenter-target distance from agglomerative clustering (4.8 cm) was slightly higher than the ground truth (4.6 cm). Similarly, the weighted agglomerative clustering achieved an average maximum ratio of 15.1 compared to the ground truth of 14.6. Notably, both k-means and weighted k-means clustering showed close agreement (within a precision of 0.1) with the ground truth for average root-mean-square target-isocenter distance and ratio (3.6 cm and 11.1, respectively). Significance. These results demonstrate the Framework’s effectiveness in generating clusters for SRS targets. The proposed approach has the potential to become a valuable tool in SRS treatment planning. Furthermore, this study is the first to investigate clustering algorithms for both minimizing maximum and sum-of-squares uncertainty in SRS.

Optical molecular imaging in cancer research: current impact and future prospect

Abstract Cancer has long been a major threat to human health. Recent advancements in molecular imaging have revolutionized cancer research by enabling early and precise disease localization, essential for effective management. In particular, optical molecular imaging is an invaluable cancer detection tool in preoperative planning, intraoperative guidance, and postoperative monitoring owing to its noninvasive nature, rapid turnover, safety, and ease of use. The tumor microenvironment and cells within it express distinct biomarkers. Optical imaging technology leverages these markers to differentiate tumor tissues from surrounding tissues and capture real-time images with high resolution. Nevertheless, a robust understanding of these cancer-related molecules and their dynamic changes is crucial for effectively managing cancer. Recent advancements in optical molecular imaging technologies offer novel approaches for cancer investigation in research and practice. This review investigates the modern optical molecular imaging techniques employed in both preclinical and clinical research, including bioluminescence, fluorescence, chemiluminescence, photoacoustic imaging, and Raman spectroscopy. We explore the current paradigm of optical molecular imaging modalities, their current status in preclinical cancer research and clinical applications, and future perspectives in the fields of cancer research and treatment.

Soil erosion susceptibility prediction using ensemble hybrid models with multicriteria decision-making analysis: Case study of the Medjerda basin, northern Africa

Soil erosion is considered one of the most prevalent natural hazards in semiarid regions, leading to the instability of ecosystems and human life. The main purpose of this research was to investigate and analyze soil erosion susceptibility maps in the Medjerda basin in northern Africa. This study utilizes four ensemble models based on the analytical hierarchy process (AHP) multicriteria decision-making analysis, namely, deep learning neural network AHP (DLNN-AHP), frequency ratio AHP (FR-AHP), Monte Carlo AHP (MC-AHP), and fuzzy AHP (F-AHP). Eight predictor variables were considered as inputs to the model, namely, the slope degree, digital elevation model (DEM), topographic wetness index (TWI), distance to river (DFR), distance to road (DFRD), normalized difference vegetation index (NDVI), rainfall erosivity (R), factor and soil erodibility factor (K). Soil erosion inventory maps were developed from field surveys and satellite images. The dataset was randomly divided into 70% for training and 30% for testing. The performances of the utilized models were compared using a receiver operating characteristic (ROC) curve. The results highlighted that all the models utilized exhibited good performance, with DLNN-AHP (93.1%) exhibiting slight superiority, followed by FR-AHP (90.9%), F-AHP (88.9%), and MC-AHP (88.5%). Among the influencing factors, the distance to the river and rainfall erosivity had the most significant impacts on the incidence of soil erosion. Moreover, the current findings revealed that 38.3% of the study area is extremely highly susceptible to soil erosion. The results of this study can aid in developing decision-support tools for planners and managers aiming to mitigate the adverse effects of soil erosion.

Planning in the age of acceleration: a perspective on digital inclusion in online urban planning meetings

In the age of technological acceleration, new digital shifts and the increased use of ICT have changed the ways we work, live, sleep, and shop. Remarkable transformations have left footprints in the planning world as well, with many urban planners harnessing technology to improve and expedite planning processes. This process accelerated further during the COVID pandemic, which forced many planning committees and local governments to conduct public meetings, hearings, and participatory processes remotely in order to allow the planning machine to continue rolling while abiding by social distancing rules. Developments such as this have been part of a broader shift toward the increasing reliance of planning on video-conferencing and other technological innovations. While this new policy has proved advantageous to many, it has also had regressive impacts and severely affected social inclusion in the planning process. This paper reviews these outcomes by focusing on the Israeli planning system post-COVID, which continues to embrace videoconferencing as a tool in planning. The findings illustrate the vulnerability of certain groups to the accelerated digitalization of urban planning. Despite planners’ awareness of these outcomes and adaptations made to existing means of e-participation, online planning meetings are not geared toward using tools and platforms to improve practice; instead, remote participation remains largely a ‘pro-developers’ process and could marginalize other participants.

A Spatial Case-Based Reasoning Method for Healthy City Assessment: A Case Study of Middle Layer Super Output Areas (MSOAs) in Birmingham, England

Assessing healthy cities is a crucial strategy for realizing the concept of “health in all policies”. However, most current quantitative assessment methods for healthy cities are predominantly city-level and often overlook intra-urban evaluations. Building on the concept of geographic spatial case-based reasoning (CBR), we present an innovative healthy city spatial case-based reasoning (HCSCBR) model. This model comprehensively integrates spatial relationships and attribute characteristics that impact urban health. We conducted experiments using a detailed multi-source dataset of health environment determinants for middle-layer super output areas (MSOAs) in Birmingham, England. The results demonstrate that our method surpasses traditional data mining techniques in classification performance, offering greater accuracy and efficiency than conventional CBR models. The flexibility of this method permits its application not only in intra-city health evaluations but also in extending to inter-city assessments. Our research concludes that the HCSCBR model significantly improves the precision and reliability of healthy city assessments by incorporating spatial relationships. Additionally, the model’s adaptability and efficiency render it a valuable tool for urban planners and public health researchers. Future research will focus on integrating the temporal dimension to further enhance and refine the healthy city evaluation model, thereby increasing its dynamism and predictive accuracy.

Transforming Education in Coastal Indonesia: A Survey of Digital Literacy and Competence Among Educators

This research explores the digital literacy and competence of educators in coastal areas of Indonesia, focusing on technology access, usage patterns, and challenges in integrating digital tools into teaching practices. Utilizing a quantitative survey method, data was collected from 235 educators teaching in coastal area schools. The study found a generally positive trend in technology access, with reliable availability of computers, laptops, and internet connection supporting educators' professional tasks. However, gaps in access to up-to-date software highlight the need for improved resources and support to ensure equitable technology use. Educators demonstrated a strong trend towards integrating digital tools in lesson planning and communication with students. Despite this, the uneven adoption of these tools suggests a need for broader and more equitable technology integration. Significant challenges include difficulties in keeping up with technological advancements and managing technical issues, emphasizing the need for comprehensive professional development and targeted training on advanced digital tools and troubleshooting. While the study provides valuable insights, it is limited by its small sample size and geographic focus, potentially not fully representing the diverse experiences of educators across Indonesia. Future research should expand the sample size and geographic range to offer a more comprehensive understanding of technology integration challenges and to support more effective recommendations for enhancing technology use in education.

Addressing the sustainable urbanism paradox: tipping points for the operational reconciliation of dense and green morphologies

To improve biodiversity and human living conditions in the Anthropocene, urban forms must reconcile density with vegetation to meet the dual sustainability-liveability challenge. This paradox poses a dilemma for urban planners and is a critical research issue requiring comprehensive analyses. Multi-family residential housing holds the potential to achieve balanced density-greening, proximity ecosystem services and human-nature connectedness, but meeting such objectives relies on finding balanced morphologies and metrics at an operational scale. Analysing 11,593 plots in the Lyon metropolitan area (France) using a systemic approach, we identified critical tipping points in morphology and greening. Density explained only 6% of Plot Greening, while morphology and landscaping accounted for 94%. We identified an open-space ratio (unbuilt area/floor area) >0.3 as a morphological threshold to achieve sustainable green supply. Operational morphologies balancing density and greening were modelled and illustrated across building heights, providing guidelines for emerging regulatory tools in sustainable urban planning.

Environmental and economic evaluation of urban building-integrated photovoltaic and electric vehicle system

Facade photovoltaic (FaPV) panels can significantly enhance the capability of building-integrated photovoltaic (BIPV) systems. This study presents a comprehensive analytical framework for optimizing the operation of BIPV and electric vehicle (EV) integrated systems and evaluating their environmental and economic benefits. The Energy-Environmental-Economic (3E) parameters are measured for alternative scenarios, and sensitivity analyses are applied to validate the impacts of changes in major parameters on BIPV-EV system's performance in Xiong'an New Area (XANA). The simulation results demonstrate that BIPV-EV systems can effectively reduce CO2 emissions and electricity costs. Specifically, the installation of FaPV can increase power generation by up to 67.60 % compared to the BIPV system utilizing stand-alone rooftop photovoltaic (RPV). Regarding urban decarbonization, the adoption of BIPV system can reduce CO2 emissions by 41.91 % and 34.99 % in the short- and long-term, respectively. The evaluation of the levelized cost of energy (LCOE) across different scenarios reveal that the configurations of RPV + FaPV (SE)-EV system and RPV + FaPV (SW)-EV system are the most cost-effective options in both the short- and long-term, where BIPV contributes to 48.10 % and 31.90 % of the total electricity generation, respectively. In terms of EV power consumption, the BIPV-EV system can decrease the grid sell ratio by 15.38 % compared to the stand-alone BIPV scenario. Compared to the BIPV system with typical RPV, the appropriate utilization of FaPV can significantly increase the potential of a BIPV system and mitigate the additional electricity demand for EVs. For cities with nascent BIPV and EV markets, the proposed framework can be a strategic tool for urban planners to design BIPV-EV systems, with the objective of enhancing sustainability and energy efficiency.

Uncovering Spatial Patterns of Residential Settlements, Segregation, and Vulnerability of Urban Seniors Using Geospatial Analytics and Modeling Techniques

We harnessed the analytical power of geospatial analysis and modeling techniques, pioneering a novel approach to studying the spatial distribution of households with at least one senior aged 65 or over, known as senior households (SHHs). We developed and implemented a spatial modeling approach that disaggregates the senior household counts obtained from the U.S. Census block data to residential land parcels. This innovative method models the senior household count on each land parcel, generating a detailed, reliable synthetic microspatial dataset for urban senior studies. Robust spatial analysis techniques are then applied to examine senior household spatial distribution in the context of segregation and access vulnerability in the study area. Our research, conducted in the urban setting of Colorado Springs, provides a deeper understanding of the senior residential landscape and identifies vulnerability. Our research findings are not just academic but also practical tools for planners, policymakers, and community advocates in the city to understand senior residential settlements, segregation, and social inequality. Our adaptable approach can be applied using similar modeling and analysis techniques for different study areas. The synthetic data and its generation approach are valuable resources for future urban senior research, enabling further examinations of different senior residential neighborhood typologies beyond basic demographic and socioeconomic aggregated indicators. They can assist studies interested in analyzing the influence of local factors and neighborhood effects on active aging among individuals, as well as simulating individual senior choices and spatial behaviors. Furthermore, they serve as a comparison baseline for future attempts to develop synthetic senior population data with socioeconomic and health characteristics.

Adaptive Nighttime-Light-Based Building Stock Assessment Framework for Future Environmentally Sustainable Management

The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally sustainable retrofitting strategies. This study presents a building stock estimation enhancement framework (BSEEF) that leverages nighttime light (NTL) to accurately assess and spatially map building stocks. By innovatively integrating a region classification module with a hybrid region-specified self-optimization module, BSEEF adaptively enhances the estimation accuracy across diverse urban landscapes. A comparative case study of Japan demonstrated that BSEEF significantly outperformed a traditional linear regression model, with improvements ranging from 1.81% to 16.75% across different metrics used for assessment, providing more accurate building stock estimates. BSEEF enhances environment/sustainability studies by enabling precise spatial analysis of built environment stocks, offering a versatile and robust framework that adapts to technological changes and achieves superior accuracy without extensive reliance on complex datasets. These advances will make BSEEF an indispensable tool in strategic planning for urban development, promoting sustainable and resilient communities globally.

UAV-based DEM augmentation using ConSinGAN for efficient flood parameter prediction with machine learning and 1D hydrodynamic models

Flooding disasters have a significant financial impact on metropolitan regions, highlighting the critical need for accurate flood prediction methods. This study addresses the problem of limited datasets in flood prediction by employing a novel approach that integrates Unmanned Aerial Vehicle (UAV)-based Digital Elevation Models (DEMs) and the ConSinGAN generative model to enhance flood prediction accuracy. The methodology involves synthesizing DEMs using ConSinGAN to augment small datasets, followed by the application of three machine learning (ML) algorithms—Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN)—to predict crucial flood-related parameters such as discharge and elevation. The predictive performance of the models was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), with units specified as meters for elevation and cubic meters per second for discharge. The RF model exhibited superior predictive capabilities, achieving a training RMSE of 0.68 cubic meters per second and an MAE of 0.30 cubic meters per second for discharge predictions. For elevation predictions, the RF model achieved a training RMSE of 0.0101 m and an MAE of 0.0046 m. Cross-validation results indicated the lowest RMSE values of 2.44 cubic meters per second for discharge and 0.03 m for elevation, with corresponding MAE values of 1.37 cubic meters per second and 0.02 m, respectively. This integrated approach, combining UAV-based DEM augmentation and advanced ML techniques, provides a robust tool for urban planners and decision-makers to accurately forecast flood parameters, thereby facilitating better disaster preparedness and mitigation strategies.

Using nominal group technique to identify the planning considerations for UK Armed Forces medical personnel delivering defence engagement first aid training activities

IntroductionDefence Medical Services personnel regularly deploy overseas to deliver training activities as part of defence engagement (DE) to positively influence partners and others. There remains scope for medical planners to...

CONTRIBUTION OF GIS IN HEALTH PLANNING, CASES OF ONCOLOGY IN ALGERIA

Geographic Information Systems (GIS) have become an essential tool in health planning, allowing for the analysis and visualization of spatial data to improve healthcare. In Algeria, the use of GIS in oncological care is still in its early stages, but has the potential to greatly enhance the planning and delivery of cancer services. This study aims to assess the contribution of GIS to health planning in the provision of oncological care in Algeria. Specifically, we will examine how Network analysis can be used to identify areas with high accessibility, map out healthcare facilities and resources, and optimize the allocation of resources to improve access to cancer treatment.A review of existing literature on the use of GIS in health planning and oncological care will be conducted, along with an analysis of cancer incidence data in Algeria. GIS software will be used to map out accessibility rates, healthcare facilities, and population distribution to identify areas in need of improved oncological services. The results of this study will provide valuable insights into how GIS can be utilized to enhance health planning in the provision of oncological care in Algeria. By identifying areas with high accessibility rates and mapping out healthcare resources, policymakers and healthcare providers can better allocate resources to improve access to cancer treatment and ultimately improve patient outcomes.

Stakeholder management within PPP-arranged civil engineering megaprojects: a systematic literature review of challenges, critical success factors and stakeholder roles

PurposePublic-private partnerships (PPPs) have become integral in delivering public services and infrastructure, particularly in the context of megaprojects. This paper focuses on the interplay between stakeholder management, challenges, critical success factors (CSFs), and the overall success of PPP-arranged civil infrastructure megaprojects.Design/methodology/approachUsing the PRISMA methodology, we comprehensively analyze challenges and critical success factors (CSFs) influencing stakeholder engagement within PPP megaprojects. A focused search equation identified 595 papers, which were distilled down to 34 relevant papers and case studies. Qualitative analysis of these papers revealed 48 CSFs categorized into 11 challenges from a stakeholder management perspective, which were further delineated across public, private, and combined sectors, and then mapped along the PPP megaproject lifecycle.FindingsInformed by a diverse amalgam of civil and project management literature, this research reveals the intricate dynamics of PPP megaprojects across the globe that emphasize the critical nature of stakeholder engagement, analysis, and management practices. Key findings highlighted conflicting interests between public and private stakeholders, manifesting in challenges like project performance versus profitability. The literature emphasized instances where neglect of local community culture led to adverse social outcomes. A universal conclusion underscored the context-specific nature of challenges and CSFs, stressing the need for a holistic understanding of stakeholders and project dynamics.Research limitations/implicationsThe paper acknowledges that it focused on 34 selected papers out of 595 identified. This sample focuses on civil engineering megaprojects which may not fully represent the breadth of research in the field, potentially missing out on valuable insights from excluded studies.Practical implicationsWe believe that the compiled list of CSFs, organized according to stakeholder relationships and the project lifecycle, serves as a potent tool for managers and planners. By enabling the identification of complexity from diverse perspectives, this research allows elucidating the challenges faced by the management team in PPP megaproject.Social implicationsThis research identifies several social outcomes related to PPP megaprojects. Critical Success Factors identified as such should allow the project managers to maximize benefits for society and minimize risk and negative externalities.Originality/valueThis study contributes valuable insights for policies and practices by systematically describing challenges and related CSFs throughout the PPP megaproject lifecycle. Additionally, it addresses the nuanced aspects of internal and external stakeholder management, thereby contributing to the overall understanding and best practices required to confront complex megaprojects involving a wide range of stakeholder groups.

Rethinking urban street experiments through Lefebvre's rhythmanalysis: From vehicles and vibrancy to virtuosos

Streets, vital for vehicular and pedestrian traffic, are increasingly repurposed to enhance urban vibrancy beyond transport needs. This paper explores intentional and spontaneous street experiments, drawing on Henri Lefebvre's rhythmanalysis. Streets are seen as hosts to intersecting rhythms shaped by daily routines and mobility, with users as performers in rhythmic interactions. While initially disruptive, these experiments strive to integrate innovative elements for cohesive urban compositions. Through a musical metaphor, the paper promotes a holistic approach to identifying rhythms influenced and introduced by street experiments. It examines how these rhythms interact, presenting as synchronised dynamics, disruptive discordance, or coexistence in dissonant compositions. Combining rhythmanalysis with spatial discourse analysis, the study ‘reads’ and ‘listens’ to temporary spaces through historical records (N = 55) of seven street experiments in Hong Kong from 2016 to 2020. Findings uncover the overly abstract agendas of these experiments — exemplified through narratives involving chairs, railings, and traffic signals — often oversimplify and exclude possibilities. The paper calls for open dialogue to capture power dynamics in street infrastructure and embodied lived experiences during the transition from vehicle-centred to people-oriented streets. It suggests rhythmanalysis as an initial tool for urban planners to envision streetscapes as symphonies, fostering sensitivity to time-space dynamics.

Anatomical and Radiological Analysis of Structures of Petrous Part of the Temporal Bone and Their Impact on Area of Anterior Petrosectomy

Anterior petrosectomy is a commonly recognized approach for accessing tumors located in the petrous apex region. The essence of anterior petrosectomy lies in drilling the petrous part of the temporal bone within the Kawase quadrangle. In our study, we conducted radiological and anatomical analyses of the structures within the petrous portion of the temporal bone, evaluating their impact on the surgical field during anterior petrosectomy. We conducted an analysis of 15 anatomical specimens and 20 3D reconstructions based on computed tomography scans of the middle ear. The analyzed structures included the impression of the trigeminal nerve, the groove of the greater petrosal nerve, the arcuate eminence, and the angle between eminentia arcuata and grove for greater petrosal nerve. The mean surface area measured by radiological methods does not deviate significantly from the mean surface area measured by anatomical methods 276.265mm2 (IQR: 217.603-309.188) vs 233.21mm2 (IQR: 210.923-255.453) p=0.051. We established a threshold 195,99mm2 for radiological determination of the surface area at which another approach should be considered. Additionally, we have developed corrections for specific radiological factors to enable a better assessment of anatomical conditions. Our results indicate that preoperative assessment of anatomical conditions based on 3D reconstructions of computed tomography of the middle ear can be a valuable tool in preoperative planning of surgery on tumors in the petroclival region using the anterior petrosectomy. Further studies involving a larger sample size are necessary to validate the findings of our study.

ANALYSIS OF TIKTOK PLATFORM UTILIZATION IN BUSINESS MARKETING

An analysis of the use of the TikTok platform in business marketing strategies shows that TikTok is a popular digital marketing platform among the public. The platform offers a wide array of interesting features and an extensive database. TikTok is also a potential place to gain profits in commerce thanks to its rising popularity. The use of TikTok can significantly change human behaviour by collecting and recording various moments to provide a comprehensive picture of an event. TikTok has evolved from an entertainment platform to a strategic tool in business planning, including through effective communication strategies to change human behaviour in a big way. With its rich features and database, TikTok has great potential as an effective business marketing platform in commerce

Comparative study of construction land development suitability assessment: Insights from multiple models and driving forces

Construction land development suitability (CLDS) assessment holds paramount importance in contemporary urban planning and management. Previous CLDS studies have faced challenges such as insufficient evaluation indicators, model complexity, and limited interpretability. To address these gaps, this study applies three simplistic yet mature models from the field of geological hazard assessment - Weight of Evidence (WOE), Information Value (IV), and Frequency Ratio (FR) - to evaluate the CLDS in Wuhan, China, and examine their feasibility. In parallel, a comparative analysis with the Patch-generating Land Use Simulation (PLUS) model was conducted. The WOE, IV, and FR models demonstrate a gradual decrease in CLDS from the city center and the Yangtze and Han rivers towards the periphery. Conversely, the PLUS model indicates the highest suitability around the city's development boundaries, followed by the city center, with suburbs displaying lower suitability. In terms of accuracy, the PLUS model's CARS module outperforms others, particularly for micro-level urban expansion simulations. The IV and WOE models follow closely, manifesting a preference for macro-level urban planning. Additionally, this study pinpoints key drivers influencing CLDS, notably population density, distance to social service Points of Interest (POI) and proximity to road networks. The cross-validation of the WOE, IV, and FR models underscores their reliability, with their straightforward principles positioning them as valuable tools in urban development planning. This comparative study validates the application of geological hazard models in urban contexts and diversifies CLDS assessment methodologies.

Mapping Social Vulnerability to Multi-Hazard Scenarios: A GIS-Based Approach at the Census Tract Level

Floods and landslides cause continuous damage to ecosystems, infrastructures, and populations. Particularly, the occurrence and the existence of different natural hazards in the same territory highlight the need to improve risk mitigation strategies for local authorities and community resilience solutions for inhabitants. Analyzing and mapping social vulnerability provides information about the main features of a specific community to deal with natural events. Specifically, the interaction between multi-hazards and the socio-economic environment suggests multidisciplinary assessments that merge the physical and the socio-economic features of the affected territories, providing a useful approach to support multi-risk reduction planning. In this context, the article focuses on integrating landslide and flood hazard scenarios with social vulnerability in the Basilicata Region (southern Italy) at the census tract level. Thirteen municipalities were chosen as multi-hazard hot spots, while open-source platforms were selected for hazard and social vulnerability data collection and analyses. A geographic information system (GIS)-based approach was applied to combine different hazard scenarios with social vulnerability distribution among 1331 census tracts to detect the most vulnerable sub-municipality areas that need special attention in multi-risk reduction strategies. The results are presented in the form of maps, which provide a relevant suitable tool in local emergency planning.

End-to-End Top-Down Load Forecasting Model for Residential Consumers

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%.