Abstract

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the Conventional system, but the main drawback of this technology is condensation and inability to cater the latent load. These challenges have directed many researchers to move their emphasis towards Personalized Conditioning System which aims to generate a microclimate zone near the vicinity of occupant according to need. This study is focused on Personalized Conditioning System when it is combined with a radiant system. The objectives of this study are 1) Evaluate the performance analysis of the system in cooling mode and, 2) Evaluate the performance of PRCS for cooling mode on different radiant cooling systems. An experimental setup of an office cubicle has been designed and fabricated using radiant panels at MNIT Jaipur, India. Three cases of the experiment were designed to evaluate and compare the performance of the system which are: Only Radiant Cooled Ceiling System (RCCS), Only Personalized Radiant Cooling System (PRCS), and both PRCS and RCCS coupled. It was observed that PRCS alone is sufficient to reach the thermal comfort criteria with low energy consumption, but it was unable to maintain the background temperature. When PRCS coupled with RCCS, it gives maximum thermal comfort for high supply water temperature ranging from 17 °C to 21 °C.

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.

Abstract

Pervasive large scale land cover land use (LULC) changes leading to deforestation have enhanced concentrations of carbon dioxide in the atmosphere and significantly impacted the regional water cycle and bio-geo chemical cycles. This neceissates assessment of current level of transitions and likely future transitions for an effective decision making towards sustainable management of natural resources. Karwar taluk of Central Western Ghats has been experiencing rapid landscape changes due to unplanned developmental activities and the consequences were evident form frequent occurrence of landslides with the loss of life and property. Analysis of land cover transitions based on temporal spatial data helps more efficient land management policies. The present study aims to visualize and forecast forest land status (2000-2026) by exploring the effects of various factors such as protected areas, slope, waterbodies, proximity to roads, industries and urban centers through agent based model on land use change. The land use change inducing factors are normalized and relative weights were generated through fuzzyfication and Multi criteria Evaluation (MCE). The spatial patterns were quantified using metrics such as Number of Patches (NP), the Edge Density (ED) and Area-Weighted Mean Patch Fractal Dimension (AWMPFD). The forest cover has reduced from 73.59 % (2000) to 67.32% (2016) with an increase in built-up from 3.17 (2000) to 8.65% (2016). Forest cover in 2024 would be about 60.44% with increase in built-up area 14.79%. Unplanned developmental activities with the increasing trends of urbanisation are the prime reasons for disasters such as landslides, water scarcity, etc. The technique has provided insights to unsustainable land cover transitions in the region for an integrated and holistic path of development to sustain natural resources while mitigating human made disasters.

Abstract

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

Abstract

Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. This paper attempts to evaluate the performance of \ an existing NoSQL database ’MongoDB’ with its inbuilt spatial functions with that of a SQL database with spatial extension ’PostGIS’ for two problems spatial and aggregate queries, across a range of datasets, with varying features counts. All the data in the analysis was processed In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 10x-25x which increases exponentially as the data size increases in both indexed and non-indexed operations. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.

Abstract

Current methods of building segmentation from LiDAR depends on extraction of roof planes to segment the buildings. In our work, we propose a new method to extract buildings using the wall facades. The motivation of using wall facades is based on the rise of terrestrial LiDAR compared to aerial LiDAR. The shape property is the most important information obtained from a LiDAR file. We try to use this shape property from the LiDAR file to segment buildings from the given scene. In the algorithm, the non-ground return points parallel to a facade of the potential building are combined to form multiple slices. These slices are parallel to the facade of a potential building. These slices are then merged based on the shape similarity and proximity to generate the footprint of the building. After generating the footprint of the building, we can also reconstruct the 3D model of the given building. The algorithm was tested both on simulated synthetic data containing different buildings and real world benchmark datasets. The results show complete segmentation of buildings from the synthetic datasets and promissory results from the real world datasets. The algorithm proposed is versatile in nature and can be easily adapted to different datasets with minimal modifications and tweaking. Existing works involve the use of secondary datasets like satellite images, orthophotos etc in order to aid in segmentation. Our work uses only the LiDAR data and no other secondary data to identify the buildings from the scene. Demonstration have been shown to segment partial buildings in a given scene as well. Thus, the novelty of the proposed work is non-dependence on any other form of data required except LiDAR. The applications of segmenting buildings from a given LiDAR scene can be used in urban planning, resource management and monitoring tasks.

Abstract

Online WebGIS tools and services have become widely available in the recent past to visualize and query geospatial data. These are primarily spatial visualization of aggregated attribute data across various spatial scales or geo-hierarchy levels. Most of these tools lack the temporal profile of the data. Also, these are server centric. In the era of big data, the user data has various attributes collected over time across geo graphic detail. Also user may like to do visual analysis across these attributes at various levels of aggregation. Such server centric approaches give limited maneuverability to explore the data and discover hidden patterns and insights in data. This paper presents a Spatial-OLAP tool that has been developed over existing Open Source geospatial and database tools. It computes data aggregation across user-defined geo hierarchy with dynamic visualizations in a defined framework. Parameters in menu options are not pre-computed but added on the fly based on user-given data. The tool enriches user experience by combining additional data-aggregation functions over spatial too lkits like open layers and allowing user to zoom to a set of temporal charts for a more extensive analysis of data across space and time. To show the utility of this tool, a GeoBI case study was done on a large online retail data of three products sold in different countries during January 2009. The tool helped quickly identify the top five countries with maximum sales and the payment options in decreasing order of popularity. Also, relationships between quantity of product sold across the geographic regions was also extracted. As Online retailers need to draw insights from data to understand their customers across regions, we hope this tool can benefit them in finding such answers - what their popular products are, identifying regions where there is a demand for these, understanding regional preferences and many others.

Abstract

Strong earthquakes cause a great damage to buildings and other infrastructure. It is difficult to get an overview of the situation immediately after an earthquake, to facilitate the initiation of appropriate emergency and rescue measures. Shake map provides early information on the intensity of ground shaking in a location. This data will be useful to help authorities and rescue workers for planning their work. For the purpose of study, Vijayawada city, India has been selected to generate shake map. Peak ground accelerations (PGA) are generated using ground motion prediction equations (GMPE) and the shaking intensities are also estimated for Vijayawada city. A tool for shake map has been generated using php applications. Map generation is automatically triggered by any earthquake in the vicinity of Vijayawada region and the map will be available for the public and government for post-earthquake decision making.

Abstract

etties are one of the most important structures in the coastal area, which can be used for transporting large quantities of goods and raw materials from one place to the other. Their functionality is very much essential because they are lifeline structures of the country. It is observed during the past earthquakes that jetties have been damaged even under mild shaking. Damaged and unserviceable jetties cause delay of export and import business. It directly affects the economy of particular region in terms of business, employment and growth. This clearly indicates the need to design these facilities so that they can withstand natural disasters particularly earthquakes and tsunamis. In this paper, a study has been carried out to find out the damage (D) to the Jetty. The Jetty is modeled using 2D Applied Element Method (AEM) to perform damage analysis of structure. Pushover analysis is done to get base shear vs roof displacement of building using displacement control method. Using the dissipated energy approach, damage is quantified at every displacement level and normalized to 1. A fragility curve has been developed to quantify the damage of Jetty with respect to different peak ground accelerations. The damage values were calculated for the PGA values of KHF Mandvi, NKF Jodiya and KMF Jhangi and found that the Jetty got light damage (D= 0.2), and moderate damage (D= 0.38 and 0.42) respectively.