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AutoEnergy algorithm boosts energy forecasting accuracy and speed

By PulseAugur Editorial · [1 sources] ·

A new thesis introduces AutoEnergy, a domain-tailored automated feature engineering algorithm designed to improve energy consumption forecasting. This algorithm reduces reliance on expert-driven feature design by generating interpretable features from timestamps and historical consumption data. When integrated with AutoML, AutoEnergy demonstrated significant reductions in forecasting error across eighteen real-world energy datasets, outperforming baseline methods in both accuracy and speed. Furthermore, its application with Decision-Focused Learning for a battery storage system problem led to substantial operational cost savings. AI

IMPACT This automated feature engineering approach could accelerate the development and deployment of more accurate energy forecasting models, leading to improved energy management and cost savings.

RANK_REASON The cluster contains a research paper detailing a new algorithm and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AutoEnergy algorithm boosts energy forecasting accuracy and speed

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The cluster contains a research paper detailing a new algorithm and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Nasser Alkhulaifi ·

    Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting

    arXiv:2609.35013v2 Announce Type: replace Abstract: The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (ECF) supports planning by predicting future consumption, but…

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