Solar and Terrestrial Radiation

Social Vulnerability to Environmental Hazards

Texture features for image classification and retrieval.

Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

Building energy load forecasting using Deep Neural Networks

Energy security, environmental concerns, thermal comfort, and economic matters are driving factors for the development of research on reducing energy consumption and the associated greenhouse gas emissions in every sector of the economy. Building energy consumption estimation has become a key approach to achieve the goals on energy consumption and emissions reduction. Energy performance of building is complicated since it depends on multiple variables associated to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper aims to provide an up-to-date review on the basics of building energy estimation. Regarding models, a classification for energy estimation models is proposed based on the different classifications found in the literature review. The paper focuses on models developed with whole building energy simulation software and their validation. This focus is justified because of the importance that whole building energy tools have gained on areas such as green building design, and analysis of energy conservation strategies and retrofits. Since a suitable weather file is a major component for reliably simulations, the section about weather data provides pertinent information.

A review on the basics of building energy estimation

Energy analysis of a building using artificial neural network: A review

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Various models based on regression as well as artificial neural networks have been studied for the estimation of monthly average global solar radiations. Most of the regression models generally used sunshine hour data for the estimation of global solar radiations on the horizontal surfaces, whereas maximum artificial neural network models have used multilayer feed forward network sigmoid trained with Levenberg–Marquardt back propagation algorithm with different input terminals and different hidden layer neurons. Artificial neural networks have been successfully employed in solving complex problems in various fields such as function approximation, pattern association and pattern recognition, associative memories and generation of new meaningful pattern. Comparison of regression and artificial neural network models have shown that the performance values of the artificial neural network models are better than the regression models. The mean absolute percent error (MAPE) values of the artificial neural network models are lower than those of the regression models. In addition, the R values of the artificial neural network models are higher than those of regression models. The artificial neural network offers an alternative method which cannot be underestimated.

Comparison of regression and artificial neural network models for estimation of global solar radiations

The Intergovernmental Panel on Climate Change (IPCC), Working Group II Report (2014) presents vulnerability as a pre-existing characteristic property of a system. Accordingly, indicators for 'sensitivity' and 'adaptive capacity', which are internal properties of a system, are employed to assess it. Comparatively, the IPCC 2007 report includes 'exposure', an external factor, as the third component of vulnerability. We have compared the construct of vulnerability presented in IPCC 2007 and 2014 reports. It is argued that the results of vulnerability assessment obtained by adopting IPCC 2014 framework are practically more useful for reducing current vulnerability in preparedness to deal with an uncertain future. In the process, we have articulated the novel concepts of 'selecting hazard-relevant vulnerability indicators' and 'assessing hazard-specific vulnerability'. Use of these concepts improves the contextualization of an assessment and thereby the acceptability of assessment results by the stakeholders.

https://iopscience.iop.org/article/10.1088/2515-7620/ab24ed/pdf

Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change

Despite high vulnerability, the impact of climate change on Himalayan ecosystem has not been properly investigated, primarily due to the inadequacy of observed data and the complex topography. In this study, we mapped the current vegetation distribution in Kashmir Himalayas from NOAA AVHRR and projected it under A1B SRES, RCP-4.5 and RCP-8.5 climate scenarios using the vegetation dynamics model-IBIS at a spatial resolution of 0.5°. The distribution of vegetation under the changing climate was simulated for the 21st century. Climate change projections from the PRECIS experiment using the HADRM3 model, for the Kashmir region, were validated using the observed climate data from two observatories. Both the observed as well as the projected climate data showed statistically significant trends. IBIS was validated for Kashmir Himalayas by comparing the simulated vegetation distribution with the observed distribution. The baseline simulated scenario of vegetation (1960–1990), showed 87.15 % agreement with the observed vegetation distribution, thereby increasing the credibility of the projected vegetation distribution under the changing climate over the region. According to the model projections, grasslands and tropical deciduous forests in the region would be severely affected while as savannah, shrubland, temperate evergreen broadleaf forest, boreal evergreen forest and mixed forest types would colonize the area currently under the cold desert/rock/ice land cover types. The model predicted that a substantial area of land, presently under the permanent snow and ice cover, would disappear by the end of the century which might severely impact stream flows, agriculture productivity and biodiversity in the region.

J. D. Sharma

Papers

Energy analysis of a building using artificial neural network: A review

Writer-independent off-line signature verification using surroundedness feature

Comparison of regression and artificial neural network models for estimation of global solar radiations

Applying IPCC 2014 framework for hazard-specific vulnerability assessment under climate change

Projected climate change impacts on vegetation distribution over Kashmir Himalayas