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Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This article highlights the potential conflict between electricity prices and carbon emissions and the resulting tradeoffs in carbon-aware and cost-aware load scheduling. To address this tradeoff, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.
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This content will become publicly available on June 17, 2026
SpareLLM: Automatically Selecting Task-Specific Minimum-Cost Large Language Models under Equivalence Constraint
We introduce SpareLLM, SelectingPassableAndResource-EfficientLLMs, a novel LLM framework designed to minimize the inference costs (i.e., resource-efficient) of large-scale NLP tasks while ensuring sufficient result quality (i.e., passable). It enables users to specify an equivalence constraint in terms of the equivalence of outputs to those of the most powerful LLM. SpareLLM then generates results that deviate from the outputs of this LLM only with a probability below a user-defined threshold. SpareLLM employs a profiling phase that evaluates the performance of multiple LLMs to identify those that meet the user-defined equivalence level. It optimizes the tradeoff between profiling overheads and the anticipated cost savings resulting from profiling. Moreover, SpareLLM further reduces inference costs by strategically leveraging a mix of LLMs. Our experiments on five real-world datasets show that SpareLLM achieves significant cost savings, up to 8.6x, while generating equivalent outputs in 90% of cases compared to GPT-4-Turbo. Compared to recent LLM cascading baselines, SpareLLM demonstrates a superior tradeoff between cost and accuracy, accounting for 91.1% and 83.8% of the points on the Pareto curve for OpenAI and Llama models.
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- Award ID(s):
- 2239326
- PAR ID:
- 10621145
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 26
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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