Related papers: Benchmarking Flexible Electric Loads Scheduling Al…
The amalgamation of Internet of Things and the smart grid enables the energy optimal scheduling of appliances based on user needs and dynamic energy prices. Additionally, progress in local storage technology calls for exploiting additional…
Benchmarking involves designing scientific test methods, tools, and frameworks to quantitatively and comparably assess specific performance indicators of certain test subjects. With the development of artificial intelligence, AI…
The integration of renewable sources poses challenges at the operational and economic levels of the power grid. In terms of keeping the balance between supply and demand, the usual scheme of supply following load may not be appropriate for…
Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, enable efficient battery storage optimization, and facilitate demand…
We study online scheduling to minimize total completion time with explorable uncertainty on single and multiple machines. Each job comes with an upper limit of its processing time, which could be potentially reduced by testing the job,…
As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metrics. In this work, we present a scalable framework for…
With the growing popularity of electric vehicles as a means of addressing climate change, concerns have emerged regarding their impact on electric grid management. As a result, predicting EV charging demand has become a timely and important…
We present a comparative study of different probabilistic forecasting techniques on the task of predicting the electrical load of secondary substations and cabinets located in a low voltage distribution grid, as well as their aggregated…
The electrification of heating, cooling, and transportation to reach decarbonization targets calls for a rapid expansion of renewable technologies. Due to their decentral and intermittent nature, these technologies require robust planning…
Flexibility is often claimed as a competitive advantage when proposing new network designs. However, most proposals provide only qualitative arguments for their improved support of flexibility. Quantitative arguments vary a lot among…
Benchmarking the performance of quantum optimization algorithms is crucial for identifying utility for industry-relevant use cases. Benchmarking processes vary between optimization applications and depend on user-specified goals. The…
This paper develops learning-augmented algorithms for energy trading in volatile electricity markets. The basic problem is to sell (or buy) $k$ units of energy for the highest revenue (lowest cost) over uncertain time-varying prices, which…
This paper addresses key challenges in task scheduling for multi-tenant distributed systems, including dynamic resource variation, heterogeneous tenant demands, and fairness assurance. An adaptive scheduling method based on reinforcement…
On the basis of an analysis of previous research, we present a generalized approach for measuring the difference of plans with an exemplary application to machine scheduling. Our work is motivated by the need for such measures, which are…
Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application. This paper proposes a scheduling process to start appliances with…
In recent years, there has been a significant focus on advancing the next generation of power systems. Despite these efforts, persistent challenges revolve around addressing the operational impact of uncertainty on predicted data,…
The effective allocation of cross-border trading capacities is one of the central challenges for the implementation of a pan-European internal energy market. In contrast to traditional power flow-ignorant methods like net transfer…
The evolution in the design of modern parallel platforms leads to revisit the scheduling jobs on distributed heterogeneous resources. The goal of this survey is to present the main existing algorithms, to classify them based on their…
Predictive benchmarking, the evaluation of machine learning models based on predictive performance and competitive ranking, is a central epistemic practice in machine learning research and an increasingly prominent method for scientific…
The automatic configuration of Mixed-Integer Programming (MIP) optimizers has become increasingly critical as the large number of configurations can significantly affect solver performance. Yet the lack of standardized evaluation frameworks…