Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPeak Electricity Demand and Global Warming in the Industrial and Residential areas of Pune : An Extreme Value Approach
92
0
0.0
(
0
)
Added by
Ayush Maheshwari
Publication date
2019
fields
Mathematical Statistics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Industrial and residential activities respond distinctly to electricity demand on temperature. Due to increasing temperature trend on account of global warming, its impact on peak electricity demand is a proxy for effective management of electricity infrastructure. Few studies explore the relationship between electricity demand and temperature changes in industrial areas in India mainly due to the limitation of data. The precise role of industrial and residential activities response to the temperature is not explored in sub-tropical humid climate of India. Here, we show the temperature sensitivity of industrial and residential areas in the city of Pune, Maharashtra by keeping other influencing variables on electricity demand as constant. The study seeks to estimate the behaviour of peak electricity demand with the apparent temperature (AT) using the Extreme Value Theory. Our analysis shows that industrial activities are not much influenced by the temperature whereas residential activities show around 1.5-2% change in average electricity demand with 1 degree rise in AT. Further, we show that peak electricity demand in residential areas, performed using stationary and non-stationary GEV models, are significantly influenced by the rise in temperature. The study shows that with the improvement in data collection, better planning for the future development, accounting for the climate change effects, will enhance the effectiveness of electricity distribution system. The study is limited to the geographical area of Pune. However, the methods are useful in estimating the peak power load attributed to climate change to other geographical regions located in subtropical and humid climate.
rate research
Read More
Whole-economy scenarios for limiting global warming to 1.5C suggest that direct carbon emissions in the buildings sector should decrease to almost zero by 2050, but leave unanswered the question how this could be achieved by real-world policies. We take a modelling-based approach for simulating which policy measures could induce an almost-complete decarbonisation of residential heating, the by far largest source of direct emissions in residential buildings. Under which assumptions is it possible, and how long would it take? Policy effectiveness highly depends on behavioural decision- making by households, especially in a context of deep decarbonisation and rapid transformation. We therefore use the non-equilibrium bottom-up model FTT:Heat to simulate policies for a transition towards low-carbon heating in a context of inertia and bounded rationality, focusing on the uptake of heating technologies. Results indicate that the near-zero decarbonisation is achievable by 2050, but requires substantial policy efforts. Policy mixes are projected to be more effective and robust for driving the market of efficient low-carbon technologies, compared to the reliance on a carbon tax as the only policy instrument. In combination with subsidies for renewables, near-complete decarbonisation could be achieved with a residential carbon tax of 50-200Euro/tCO2. The policy-induced technology transition would increase average heating costs faced by households initially, but could also lead to cost reductions in most world regions in the medium term. Model projections illustrate the uncertainty that is attached to household behaviour for prematurely replacing heating systems.
Observed accidents have been the main resource for road safety analysis over the past decades. Although such reliance seems quite straightforward, the rare nature of these events has made safety difficult to assess, especially for new and innovative traffic treatments. Surrogate measures of safety have allowed to step away from traditional safety performance functions and analyze safety performance without relying on accident records. In recent years, the use of extreme value theory (EV) models in combination with surrogate safety measures to estimate accident probabilities has gained popularity within the safety community. In this paper we extend existing efforts on EV for accident probability estimation for two dependent surrogate measures. Using detailed trajectory data from a driving simulator, we model the joint probability of head-on and rear-end collisions in passing maneuvers. In our estimation we account for driver specific characteristics and road infrastructure variables. We show that accounting for these factors improve the head-on collision probability estimation. This work highlights the importance of considering driver and road heterogeneity in evaluating related safety events, of relevance to interventions both for in-vehicle and infrastructure-based solutions. Such features are essential to keep up with the expectations from surrogate safety measures for the integrated analysis of accident phenomena.
It has been claimed by others that observed temporal correlations of terrestrial cloud cover with `the cosmic ray intensity are causal. The possibility arises, therefore, of a connection between cosmic rays and Global Warming. If true, the implications would be very great. We have examined this claim to look for evidence to corroborate it. So far we have not found any and so our tentative conclusions are to doubt it. Such correlations as appear are more likely to be due to the small variations in solar irradiance, which, of course, correlate with cosmic rays. We estimate that less than 15% of the 11-year cycle warming variations are due to cosmic rays and less than 2% of the warming over the last 35 years is due to this cause.
The statistics of shear peaks have been shown to provide valuable cosmological information beyond the power spectrum, and will be an important constraint of models of cosmology with the large survey areas provided by forthcoming astronomical surveys. Surveys include masked areas due to bright stars, bad pixels etc, which must be accounted for in producing constraints on cosmology from shear maps. We advocate a forward-modeling approach, where the impact of masking (and other survey artifacts) are accounted for in the theoretical prediction of cosmological parameters, rather than removed from survey data. We use masks based on the Deep Lens Survey, and explore the impact of up to 37% of the survey area being masked on LSST and DES-scale surveys. By reconstructing maps of aperture mass, the masking effect is smoothed out, resulting in up to 14% smaller statistical uncertainties compared to simply reducing the survey area by the masked area. We show that, even in the presence of large survey masks, the bias in cosmological parameter estimation produced in the forward-modeling process is ~1%, dominated by bias caused by limited simulation volume. We also explore how this potential bias scales with survey area and find that small survey areas are more significantly impacted by the differences in cosmological structure in the data and simulated volumes, due to cosmic variance.
Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in extreme value theory to characterize statistics of extreme events in terms of the maximal queue length. Subsequently, leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
