3 research outputs found
Carbon Responder: Coordinating Demand Response for the Datacenter Fleet
The increasing integration of renewable energy sources results in
fluctuations in carbon intensity throughout the day. To mitigate their carbon
footprint, datacenters can implement demand response (DR) by adjusting their
load based on grid signals. However, this presents challenges for private
datacenters with diverse workloads and services. One of the key challenges is
efficiently and fairly allocating power curtailment across different workloads.
In response to these challenges, we propose the Carbon Responder framework.
The Carbon Responder framework aims to reduce the carbon footprint of
heterogeneous workloads in datacenters by modulating their power usage. Unlike
previous studies, Carbon Responder considers both online and batch workloads
with different service level objectives and develops accurate performance
models to achieve performance-aware power allocation. The framework supports
three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and
Decentralized DR. We evaluate Carbon Responder polices using production
workload traces from a private hyperscale datacenter. Our experimental results
demonstrate that the efficient Carbon Responder policy reduces the carbon
footprint by around 2x as much compared to baseline approaches adapted from
existing methods. The fair Carbon Responder policies distribute the performance
penalties and carbon reduction responsibility fairly among workloads
Solar irradiance forecasting and energy optimization for achieving nearly net zero energy building
Solar energy and the concept of passive solar architecture are being increased in several areas to attain the net-zero energy concept. This paved the way for an increase in the need of solar irradiance forecasting for both solar PV applications and Passive Solar Architectural buildings. First, solar irradiance forecasting was done with 131 400 data sets (1-h data for 15 years) which was split into monthly mean for every year. This model was evaluated by forecasting the post-consecutive years one by one with the pre-consecutive years which includes the pre-forecasted years. This model was shown to have RMSE values of 11% to 24% for various seasonal forecasting using the Random Forest Algorithm in WEKA, which gave the annual irradiance results nearer to the PV Sol energy forecasting results. The R-value was in the range of 0.8 to 0.9 for various seasons which is good. Building Energy Optimization was carried out using BEopt 2.8 software designed by NREL. The chosen building was set to the standard parameters in India, and then, the optimization was done with various customized parameters and systems available in India to reduce the energy consumption from 192.2 MMBtu/yr to 109.1 MMBtu/yr with a 7 kW Solar PV System to attain the net-zero energy concept