> ## Documentation Index > Fetch the complete documentation index at: https://documentation.kodelabs.com/llms.txt > Use this file to discover all available pages before exploring further. # Verify energy savings with an M&V model > Train a measurement and verification model, check its accuracy, and read verified savings against an adjusted baseline. export const TutorialStep = ({tutorialId, id, number, title, stepIds = "", subStepIds = "", mediaLabel, children}) => { const progressEvent = "kode-tutorial-progress"; const brandBlues = ["#015BFA", "#3A9AFF", "#7BB3FF", "#C5DBFF", "#E8F1FF"]; const [complete, setComplete] = useState(false); const parseIds = value => { if (Array.isArray(value)) return value; if (typeof value === "string" && value.length > 0) { return value.split(",").map(stepId => stepId.trim()).filter(Boolean); } return []; }; const hasSubsteps = parseIds(subStepIds).length > 0; const readProgress = () => { if (typeof window === "undefined") return {}; try { return JSON.parse(localStorage.getItem(`kode-tutorial:${tutorialId}`) || "{}"); } catch { return {}; } }; const writeProgress = next => { if (typeof window === "undefined") return; localStorage.setItem(`kode-tutorial:${tutorialId}`, JSON.stringify(next)); window.dispatchEvent(new CustomEvent(progressEvent, { detail: { tutorialId, progress: next } })); }; const computeComplete = current => { const subs = parseIds(subStepIds); if (subs.length > 0) { return subs.every(stepId => current[stepId] === true); } return current[id] === true; }; const playConfetti = () => { if (typeof window === "undefined" || typeof document === "undefined") return; const canvas = document.createElement("canvas"); canvas.className = "tutorial-confetti-canvas"; canvas.width = window.innerWidth; canvas.height = window.innerHeight; document.body.appendChild(canvas); const ctx = canvas.getContext("2d"); const pieces = Array.from({ length: 80 }, () => ({ x: Math.random() * canvas.width, y: -20 - Math.random() * canvas.height * 0.35, w: 4 + Math.random() * 5, h: 6 + Math.random() * 8, color: brandBlues[Math.floor(Math.random() * brandBlues.length)], vy: 2.2 + Math.random() * 3.2, vx: -1.5 + Math.random() * 3, rot: Math.random() * Math.PI, vr: -0.12 + Math.random() * 0.24, alpha: 0.55 + Math.random() * 0.35 })); let frame = 0; const maxFrames = 110; const draw = () => { frame += 1; ctx.clearRect(0, 0, canvas.width, canvas.height); pieces.forEach(piece => { piece.x += piece.vx; piece.y += piece.vy; piece.rot += piece.vr; piece.vy += 0.045; const fade = Math.max(0, 1 - frame / maxFrames); ctx.save(); ctx.translate(piece.x, piece.y); ctx.rotate(piece.rot); ctx.globalAlpha = piece.alpha * fade; ctx.fillStyle = piece.color; ctx.fillRect(-piece.w / 2, -piece.h / 2, piece.w, piece.h); ctx.restore(); }); if (frame < maxFrames) { requestAnimationFrame(draw); } else { canvas.remove(); } }; requestAnimationFrame(draw); }; useEffect(() => { const current = readProgress(); setComplete(computeComplete(current)); const onProgress = event => { if (event.detail?.tutorialId === tutorialId) { setComplete(computeComplete(event.detail.progress || ({}))); } }; window.addEventListener(progressEvent, onProgress); return () => window.removeEventListener(progressEvent, onProgress); }, [tutorialId, id, subStepIds]); const toggleComplete = () => { if (hasSubsteps) return; const current = readProgress(); const nextValue = !current[id]; const next = { ...current, [id]: nextValue }; writeProgress(next); setComplete(nextValue); const topLevel = parseIds(stepIds); if (nextValue && topLevel.length > 0 && topLevel.every(stepId => next[stepId] === true)) { playConfetti(); } }; return

Step {number}

{title}

{mediaLabel ?
{mediaLabel} Replace this block with a Frame, image, or video when ready
: null}
{children}
{hasSubsteps ?

{complete ? "All substeps complete" : "Mark each substep complete to finish this step"}

: }
; }; export const TutorialProgress = ({tutorialId, steps = []}) => { const progressEvent = "kode-tutorial-progress"; const [progress, setProgress] = useState({}); const [activeId, setActiveId] = useState(steps[0]?.id || ""); const parseSubsteps = step => { if (!step?.substeps) return []; if (Array.isArray(step.substeps)) return step.substeps; if (typeof step.substeps !== "string") return []; return step.substeps.split(";").map(entry => entry.trim()).filter(Boolean).map(entry => { const sep = entry.indexOf(":"); if (sep === -1) return { id: entry, title: entry }; return { id: entry.slice(0, sep).trim(), title: entry.slice(sep + 1).trim() }; }); }; const isStepComplete = (step, current) => { const subs = parseSubsteps(step); if (subs.length > 0) { return subs.every(sub => current[sub.id] === true); } return current[step.id] === true; }; const readProgress = () => { if (typeof window === "undefined") return {}; try { return JSON.parse(localStorage.getItem(`kode-tutorial:${tutorialId}`) || "{}"); } catch { return {}; } }; const resetProgress = () => { if (typeof window === "undefined") return; localStorage.removeItem(`kode-tutorial:${tutorialId}`); window.dispatchEvent(new CustomEvent(progressEvent, { detail: { tutorialId, progress: {} } })); }; useEffect(() => { setProgress(readProgress()); const onProgress = event => { if (event.detail?.tutorialId === tutorialId) { setProgress(event.detail.progress || ({})); } }; window.addEventListener(progressEvent, onProgress); return () => window.removeEventListener(progressEvent, onProgress); }, [tutorialId]); useEffect(() => { const ids = []; steps.forEach(step => { ids.push(step.id); parseSubsteps(step).forEach(sub => ids.push(sub.id)); }); const nodes = ids.map(stepId => { return document.getElementById(`tutorial-step-${stepId}`) || document.getElementById(`tutorial-substep-${stepId}`); }).filter(Boolean); if (nodes.length === 0) return undefined; const observer = new IntersectionObserver(entries => { const visible = entries.filter(entry => entry.isIntersecting).sort((a, b) => b.intersectionRatio - a.intersectionRatio); if (visible[0]?.target?.id) { const raw = visible[0].target.id.replace("tutorial-substep-", "").replace("tutorial-step-", ""); setActiveId(raw); } }, { rootMargin: "-20% 0px -55% 0px", threshold: [0.15, 0.4, 0.7] }); nodes.forEach(node => observer.observe(node)); return () => observer.disconnect(); }, [tutorialId, steps.length]); const completedCount = steps.filter(step => isStepComplete(step, progress)).length; const done = steps.length > 0 && steps.every(step => isStepComplete(step, progress)); const pct = steps.length ? Math.round(completedCount / steps.length * 100) : 0; return ; }; export const TutorialHero = ({eyebrow = "Tutorial", title, summary, time, level, imageSrc, imageAlt = "Tutorial preview"}) => { useEffect(() => { if (typeof document === "undefined") return undefined; document.documentElement.setAttribute("data-tutorial-layout", "true"); const page = document.querySelector(".tutorial-page"); if (page) { let sibling = page.previousElementSibling; while (sibling) { sibling.setAttribute("data-tutorial-hidden-header", "true"); sibling = sibling.previousElementSibling; } } document.querySelectorAll("h1").forEach(heading => { if (heading.textContent.trim() !== title) return; heading.setAttribute("data-tutorial-hidden-header", "true"); const prev = heading.previousElementSibling; if (prev) prev.setAttribute("data-tutorial-hidden-header", "true"); const next = heading.nextElementSibling; if (next && next.tagName === "P") { next.setAttribute("data-tutorial-hidden-header", "true"); } }); return () => { document.documentElement.removeAttribute("data-tutorial-layout"); document.querySelectorAll("[data-tutorial-hidden-header]").forEach(node => node.removeAttribute("data-tutorial-hidden-header")); }; }, [title]); return
; };
This walkthrough verifies savings for **one building** that already has energy data flowing into EnerG. You train a model, confirm it is accurate, then read the savings it reports. If you have not set up a building yet, complete [Set up your first building](/products/energ/tutorials/set-up-your-first-building) first. To understand the method before you click, read [Energy modeling and M\&V](/products/energ/concepts/energy-modeling). Make sure you have the following: * **A configured building** — meters mapped and a qualifying [baseline](/products/energ/concepts/baselines-and-weather-normalization) in place * **Enough history** — at least 12 consecutive months of complete billed data, or interval data for meter-level work * **A known measure** — the retrofit or operational change whose savings you want to verify M\&V compares later usage against an **adjusted baseline**: what the building would have used without the measure, under the same conditions. IPMVP defines how you draw the measurement boundary. EnerG supports two options. * **Option B** — model savings at the meter or system level when a measure targets specific equipment with dedicated metering. * **Option C** — model savings at the whole building when a retrofit or program is best measured at billing boundaries. Choose Option B when metering isolation is strong. Choose Option C when boundary-level bills represent performance. For the trade-offs, see [IPMVP options](/products/energ/concepts/energy-modeling#ipmvp-options). Open the building view, then start a new model from the modeling workspace. In the building-level left sidebar, expand `Energy Modeling` and open the modeling workspace. Click `+ Create Model`, then give the model a clear name that identifies the building and the measure. Select the IPMVP option you chose in the previous step. For the full field-by-field walkthrough, see [Create an M\&V model](/products/energ/energy-modeling/mv-models). Baselines page with Energy, Water, and Waste sections each showing the auto-configure option, and an Edit Configuration button in the top-right corner Choose the meters that carry the measure's signal, then set the baseline period the model trains on. For Option B, select the isolated system meters. For Option C, select the whole-building meters. Confirm each meter has `Include in Metrics` enabled. Pick a baseline window that reflects normal operation before the measure. A baseline of 12 months captures a full weather cycle. If a window will not qualify, close gaps first. See [Data completeness](/products/energ/data-quality/data-completeness). Drivers are the variables the model regresses energy against, so it separates real savings from noise. Add weather as degree days, then add any operational drivers you track, such as occupancy or production. Start with weather alone, then add drivers only if they improve fit. Click `Train` to fit the model over the baseline period. Training runs in the background and returns accuracy metrics when it finishes. A model is only useful if it fits the baseline well. EnerG reports three accuracy metrics. * **R-squared** — the share of variation the model explains. Higher is better. * **CV-RMSE** — the scatter of predictions around actuals. Lower is better. * **MAPE** — the average percent error. Lower is better. Compare each value against your thresholds in [Calculations and parameters](/products/energ/reference/calculations-and-parameters#model-accuracy). If the model falls short, widen the baseline, adjust drivers, or improve meter completeness, then train again. Do not report savings from a model that fails your accuracy thresholds. A poor fit produces an unreliable adjusted baseline. With an accurate model, open the analysis view to read savings against the adjusted baseline. The chart plots the **adjusted baseline** against **actual usage** for the reporting period. The gap between them is your verified savings, expressed in energy, cost, and avoided emissions. For every field on this view, see [M\&V analysis](/products/energ/energy-modeling/mv-analysis). To project the trained model forward, continue to [Forecasting](/products/energ/energy-modeling/forecasting).
## What's next Review IPMVP options, adjusted baselines, and model fit in depth. Read every field on the savings and adjusted baseline view. Project one to 12 months of consumption from your trained model. Link verified savings to measures and scenarios that shape budgets.