Related papers: Future Energy Consumption Prediction Based on Grey…
With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone. Doing so can improve people's livelihoods, health, and ecosystems for the present and…
Meteorological factors (MF) are crucial in day-ahead load forecasting as they significantly influence the electricity consumption behaviors of consumers. Numerous studies have incorporated MF into the load forecasting model to achieve…
The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs…
Envisioning a future 100% electrified transportation sector, this paper uses socio-economic, demographic, and geographic data to assess electric energy demand from commuter traffic. We explore the individual mode choices, which allows to…
Conventional energy production based on fossil fuels causes emissions which contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, an endeavor that requires…
A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would…
The energy consumption and carbon footprint of Artificial Intelligence (AI) have become critical concerns due to rising costs and environmental impacts. In response, a new trend in green AI is emerging, shifting from the "bigger is better"…
This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area.…
Forecasting electricity demand plays a fundamental role in the operation and planning procedures of power systems and the publications about electricity demand forecasting increasing year by year. In this paper, we use Scientometric…
This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preserving microscopic level accuracy. For this work, we leveraged a…
Accurate electrical consumption forecasting is crucial for efficient energy management and resource allocation. While traditional time series forecasting relies on historical patterns and temporal dependencies, incorporating external…
Computer models are widely used in decision support for energy systems operation, planning and policy. A system of models is often employed, where model inputs themselves arise from other computer models, with each model being developed by…
Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and…
The electric power sector is a leading source of air pollutant emissions, impacting the public health of nearly every community. Although regulatory measures have reduced air pollutants, fossil fuels remain a significant component of the…
The United States has witnessed a growing prevalence of droughts in recent years, posing significant challenges to water supplies and power generation. The resulting impacts on power systems, including reduced capacity and the potential for…
Fog computing allows computationally-heavy problems with tight time constraints to be solved even if end devices have limited computational resources and latency induced by cloud computing is too high. How can energy consumed by fog…
Constraints on the expansion history of the universe from measurements of cosmological distances make predictions for large-scale structure growth. Since these predictions depend on assumptions about dark energy evolution and spatial…
Concerns about the environmental footprint of machine learning are increasing. While studies of energy use and emissions of ML models are a growing subfield, most ML researchers and developers still do not incorporate energy measurement as…
We propose a simple empirical scaling law that describes load forecasting accuracy at different levels of aggregation. The model is justified based on a simple decomposition of individual consumption patterns. We show that for different…
In this paper, we propose to model the energy consumption of smart grid households with energy storage systems as an intertemporal trading economy. Intertemporal trade refers to transaction of goods across time when an agent, at any time,…