Abstract

In early stage of building design, design team has to consider and simulate energy consumption for several combinations of various input parameters to analyze the building energy consumption. In a scenario considering five parameters, each with ten variations, one has to simulate hundred thousand combinations. It requires a lot of computation to simulate energy consumption for all the input combinations. This paper aims at reducing the computation required to compute the energy consumption of all the combinations. This is done by identifying appropriate training samples, computing their energy consumption using EnergyPlus and estimating energy consumption of the rest of the data using machine learning techniques. This paper presents two sampling methods along with various regression techniques to predict energy consumption of a building in the early phase. It involves usage of efficient sampling methods for identifying the training data. The key contribution of this method of surrogate modeling is saving a lot of computation by reducing the computation by ~100-fold. This method is tested for Jaipur and Hyderabad cities of India. Approximately hundred thousand simulations are performed for each location using parallel computation. By simulating approximately one percent of the input combinations, annual energy consumption for the large set of combinations are predicted using SVR and kmeans clustering for Jaipur with accuracy greater than 93% for 99.8% of the input combinations. When the same model is trained for Hyderabad, it produced accuracy greater than 93% for 98% of the input combinations.

Abstract

Based on recent strategies in peak demand reduction for HVAC systems, simple measures like increasing cooling set-point temperatures serves as an effective Demand Response (DR). Majority of the past studies in demand response focus majorly on developing strategies that reduce peak demand and on demand side energy management to optimize energy consumption with the help of renewable energy resources. Research in estimating the potential of DR programs is required and is gaining momentum. It is essential to develop reliable estimation models that can be applied in realtime. We therefore focus on developing a model that predicts the impact of change in HVAC set-point temperature on cooling energy demand. During model evaluation, we made an observation that after a DR event when the set-points are back to normal schedule, sudden and rapid peaks occur in the demand while it is ramping up as set-point temperatures are reduced. For buildings which have a prescribed demand limit, these peaks cause huge demand penalty. We further propose a strategy to enable a stable ramping up process.

Abstract

he dry evergreen forest of Kondapalli (Andhra Pradesh state, India) is declared as a forest reserve, but, despite of this, it is subjected to degradation resulting in loss of biodiversity. Thus, the current study was carried out to investigate the tree diversity of Kondapalli forest. A total of 566 ±16 trees (≥10 cm) representing 46±8 species from 40 genera and 21 families were recorded from the 0.36 ha area of Kondapalli forest. Mimosaceae was the most species rich family, while Rutaceae was the most abundant family. Atalantia monophylla was most frequent and abundant species and, with respect to basal area, Melia azedarach and Syzygium cumini were the dominant taxa. The recorded stem density was 1572 stems ha−1 and the mean forest basal area was 47.17 m2 ha−1. The results of cluster analysis revealed that Atalantia monophylla, characterised by a high ecological amplitude, had a wide distribution and was associated with species forming different communities. The study showed that Kondapalli forest is characterised by a fairly high species richness, which provides the baseline data on the floristic structure and diversity of this forest for better management and conservation.

Abstract

The use of in situ hyperspectral reflectance and bio-physical measurements has been increasing in forestry. Due to limited physical accessibility in a forest environment, most of the reflectance measurements of trees are acquired at a leaf or bunch of leaves level. A few radiative transfer models are available for upscaling leaf spectra to canopy level. While these models are sophisticated, they retrieve canopy spectra based on certain assumptions. We propose ‘semi-empirical model for upscaling leaf spectra (SEMULS)’ which upscales in situ leaf spectra to canopy level based on the relationship between leaf spectra and its bio-physical parameters. The performance of the model has been quantitatively validated by comparing the upscaled canopy spectra with spectra from – CHRIS hyperspectral imagery acquired concurrently and from the PROSAIL model. Results indicate that the SEMULS retrievals are …

Abstract

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policy makers in utilizing such data to study and counter sexism better. The existing work on sexism classification, which is different from sexism detection, has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s), and we contribute the largest dataset for sexism categorization. We develop a neural solution for this multilabel classification that can combine sentence representations obtained using models such as BERT with distributional and linguistic word embeddings using a flexible, hierarchical architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. The best proposed method outperforms several deep learning as well as traditional machine learning baselines by an appreciable margin.

Abstract

Analysis of high variability of inter-state land use land cover change using remote sensing and GIS techniques primarily agriculture in Krishna river basin, India.

Abstract

By 2050 most parts of India will be water stressed zones as most of the water resources are under heavy stress due to increasing nutrient contamination in their waters. In this scenario, studying the changes occurring in the freshwater nutrient contamination levels over a temporal scale is extremely important. This study focuses on monitoring the changes occurring in the nutrient contamination levels over a decade in a large reservoir known as Nagarjuna Sagar (NS) using remote sensing data. In this study, Landsat (5 & 8) data for the year 2005, 2009, 2015 and Sentinel (2A and 2B) data for the years 2016 and 2018 is used to study nutrient contamination in NS. The spatial spread of chlorophyll-a (chl-a) area is used as a proxy to estimate the extent of nutrient contamination in NS. In this study, only October images of NS are used as they exhibit the maximum spatial spread of Chl-a and hence help assess the contamination levels over the period 2005-2018. The analysis shows that during this period, chl-a spatial spread area has increased from 21 Km2 to 205 Km2, indicating a decrease in water quality in the reservoir. The study shows that this is accompanied by an increase in the agricultural land use area by 1000 Km2 in addition to a steep increase in the use of agricultural inputs, primarily fertilisers like urea, P and K. Thus, while the combined effect of excessive usage of fertilizers with agricultural intensification has increased crop yields, it has also contributed to damaging the freshwater resources.

Abstract

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone. View Full-Text

Abstract

River water bodies serve as prominent water sources for various purposes ranging from drinking water, waste load allocation, irrigation, hydropower generation and ecosystem services. Human activities and natural processes require a balanced water supply and demand, while population growth, land use and climate change are the external forces which try to change the stream and river water quan-tity and quality. Water temperature is an inherent property of its quality and a con-trolling factor of health of freshwater environments. It is often considered as a driver of metabolic activity in water bodies, which influence the biological and chemical processes affecting the metabolic responses from organisms to ecosystems. The present work aims to explore sources of predictability of river water temperature (RWT) as a keen driver of hydrological and ecological processes at multiple scales. Increasing RWT in response to climate change and local-to-regional anthropogenic activities result in decreasing dissolved oxygen (DO) levels and anaerobic condi-tions in the aquatic system, thereby affecting marine life and the consequent avail-ability of food, reproduction and migration. An assessment of integrated RWT and streamflow fluctuations is proposed to evidence biological activity, chemical specia-tion, oxygen solubility and self-purification capacity of a river system and fluctua-tions of flows responsive to hydro-climate pulses. The independent and integrated contributions of air temperatures and flow fluctuations to RWT in the Missouri River near Nebraska City, USA, represent the stream responses to global raising temperatures. To quantify the contributions of multiple variations of predictor vari-ables in the air-water interfaces to RWT variability, we use a multiple regression. The performance of the model was tested along Missouri River near Nebraska City, USA, using historical series of daily river water temperature, air temperature and river discharges for the 1947–2014 period. A sensitivity analysis on river water temperature is performed, under air temperature increase of +2 °C, +4 °C and + 6 °C with a decrease of discharge of ±20%. Overall, the increase of RWT for the Missouri River is observed as about 2.76 °C under various air temperature and discharge changes when compared with the observed conditions at mean annual scale. The study results provide a comprehensive analysis of the impacts of river discharge and air temperature changes under climate change over RWT.

Abstract

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygendemand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains,and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under theimpact of various scenarios. Corresponding to each scenario, minimum Eflow throughout the river stretch—that is, the headwaters flow(quantity and quality) meeting desirable river water quality standards (as proposed by the Indian Central Pollution Control Board)—was explored. Water quality charts were developed for direct and user-friendly estimation of Eflows. The proposed approach was applied to the Yamuna and Bhadra rivers in India. The results reveal the inefficiency of existing river flow conditions in maintaining permissible water quality standards. Adverse effects of pollution load, upstream diversions, and climate change are highlighted.