[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-ai-feature-engineering-trims-battery-storage-costs-up-to-57":10,"sections":40},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":30,"tags":31,"sources":35,"feedback":39,"feedback_at":22,"cost_usd":39,"total_tokens":39},10087,"ai-feature-engineering-trims-battery-storage-costs-up-to-57","AI Feature Engineering Trims Battery Storage Costs Up to 57%","AutoEnergy, a new automated feature-engineering method, trims battery storage operating costs by up to 57 percent in UK property tests.","An automated feature-engineering system called AutoEnergy is making energy forecasting models sharper and battery storage cheaper to operate.\n\nAutoEnergy automatically generates features from timestamps and past consumption data, then hands them to AutoML systems for forecasting. Tested across eighteen real-world energy datasets spanning homes, businesses, factories, renewables, and grid operators, it cut forecasting error by 19.52 to 84.72 percent compared with baseline AutoML and existing automated feature-engineering methods, while running 1.31 to 4.41 times faster. The researchers then paired AutoEnergy with a decision-focused learning approach for battery energy storage systems, which forecasts electricity prices and demand while simultaneously optimizing charging and discharging. On a real UK property dataset, that combination cut operating costs by up to 57 percent compared with the same decision-focused models run without automated feature engineering.\n\nThat second result is the more interesting one. Forecasting accuracy numbers are cheap; plenty of papers report error reductions that never touch a real system. Showing those gains survive the trip from prediction to an actual cost-optimization decision is a rarer and more useful claim, especially for something as unglamorous and expensive as feature engineering, which usually eats a data scientist's time rather than a spreadsheet's error margin.\n\nStill, this is one dataset and one battery setup, and the savings range (22.9 to 56.5 percent) is wide enough to suggest results depend heavily on the property in question. Worth watching, not worth rewriting your energy-management stack around yet.","[\"ai\",\"automl\",\"energy\",\"machine-learning\"]","2026-10-05T04:00:00.000Z","2026-10-05T22:24:08.012Z","2026-10-05T22:24:13.804Z","published",null,[24],{"id":25,"reviewer":26,"round":27,"reason":28,"status":29},"editor-r1","editor",1,"Fix the dek: it claims storage costs are trimmed 'by over half,' but the actual range is 22.9%-56.5%, so state it as 'by up to 57 percent' to match the source and avoid overstating the low end of the range.","resolved","ai",[30,32,33,34],"automl","energy","machine-learning",[36],{"name":37,"url":38},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.35013",0,{"sections":41},[42,45,49,54,59,64,68,73,77,82,87,92,97,102],{"name":43,"slug":30,"count":44,"latest_published_at":18},"AI",6314,{"name":46,"slug":47,"count":48,"latest_published_at":18},"Security","security",871,{"name":50,"slug":51,"count":52,"latest_published_at":53},"Policy","policy",444,"2026-10-03T15:02:01.000Z",{"name":55,"slug":56,"count":57,"latest_published_at":58},"Deals","deals",325,"2026-10-04T13:00:00.000Z",{"name":60,"slug":61,"count":62,"latest_published_at":63},"Hardware","hardware",204,"2026-10-03T14:50:50.000Z",{"name":65,"slug":66,"count":67,"latest_published_at":18},"Science","science",179,{"name":69,"slug":70,"count":71,"latest_published_at":72},"Consumer Tech","consumer-tech",158,"2026-10-03T03:21:12.000Z",{"name":74,"slug":75,"count":76,"latest_published_at":18},"Dev Tools","dev-tools",98,{"name":78,"slug":79,"count":80,"latest_published_at":81},"Software","software",97,"2026-10-04T10:00:00.000Z",{"name":83,"slug":84,"count":85,"latest_published_at":86},"Startups","startups",92,"2026-10-04T14:36:25.000Z",{"name":88,"slug":89,"count":90,"latest_published_at":91},"Gaming","gaming",53,"2026-10-02T02:50:39.000Z",{"name":93,"slug":94,"count":95,"latest_published_at":96},"General","general",51,"2026-10-05T02:35:01.000Z",{"name":98,"slug":99,"count":100,"latest_published_at":101},"Reviews","reviews",32,"2026-10-02T18:00:00.000Z",{"name":103,"slug":104,"count":105,"latest_published_at":106},"How-To","how-to",7,"2026-10-01T09:00:00.000Z"]