Forecasting

Train a forecast configuration to project near-term energy usage for a building from interval meter data. In the building view, expand Energy Modeling in the left sidebar and open Forecast. Results also appear on the Building Energy dashboard when you select Forecast under Energy Modeling. For how forecasting extends trained models, see Energy modeling and M&V.

Forecasts rely on interval reads with enough history for the model to learn daily and weekly patterns. Confirm meter completeness on the Meters page before you train.

Open Forecast

Select a building

From Portfolio Home, open a building in the building view.

Open Energy Modeling

In the left sidebar, expand Energy Modeling, then click Forecast.

Manage configurations

The Forecast page lists every forecast configuration for the building. The page subtitle describes the default horizon: forecast energy usage trends over the coming week. Click + Create Configuration to start the three-step wizard. Use the Actions menu on a row to edit or delete an existing configuration.

Forecast page for Headquarters showing configurations table with Name Forecast Default tag Aggregation Hourly Meters count 1 Status Succeeded Actions menu and Create Configuration button — Forecast configurations list with aggregation and status

Column	What it shows
Name	Configuration label. The Default tag marks the configuration EnerG uses on forecast dashboards
Aggregation	Rollup level, such as Hourly
Meters	Count of meters included
Status	Training state, such as Succeeded
Actions	Row menu for edit, delete, or related tasks

Create a forecast configuration

Click + Create Configuration to open the wizard. Progress appears in the left sidebar across three steps: Basic Setup, Data Selection, and Summary.

Step 1: Basic setup

Define the configuration name and whether it is the default forecast for the building.

Forecast wizard step 1 of 3 Basic Setup with Name Forecast Default checkbox checked optional Description field and Next button — Forecast wizard step 1 Basic Setup

Field	Purpose
Name	Label shown in lists and dashboard filters
Default	When checked, EnerG selects this configuration on forecast views
Description	Optional notes for your team

Click Next to continue.

Step 2: Data selection

Choose aggregation, training start date, interval meters, and accuracy thresholds.

Field	Purpose
Aggregation	Granularity for the model, such as Hourly
Start Date	Optional date to limit training data. Leave empty to use all available history
Meter list	Search and filter by Meter Scope and Service Type, then select meters
Point selected	For interval meters, pick the point type EnerG uses for the forecast, such as an electric accumulator
Threshold Percentage	Model Metrics uses Mean Absolute Percentage Error (MAPE) as the threshold, or Set the Threshold for a custom percent

Many teams use Hourly aggregation when they need a week-ahead operational view. Match the point type to the same signal you use in meter comparisons.

Step 3: Summary

Review trained model metrics before you finish.

The summary confirms how many meters trained (Meters Included versus Meters Selected) and the Last Trained date. It also reports CVRMSE, MAPE, and R Squared fit. For metric definitions and thresholds, see Model accuracy thresholds. Click Finish to save the configuration. Projected consumption appears on forecast charts after processing completes.

Read forecast results

Open Forecast from the Building Energy dashboard secondary menu to compare projected consumption to actual use for recent and upcoming periods. Charts update after the scheduled sync cycle, similar to M&V analysis results.

When to use forecasting

Use case	Approach
Near-term operations	Train an Hourly configuration to see usage trends over the coming week
Peak load planning	Compare projected peaks to actual interval data after major equipment changes
Budget checks	Align forecast horizons with monthly budget periods on Building budget overview
Savings validation	Pair forecasts with M&V when you need both adjusted baselines and forward projections

When training fails

Forecast runs can fail when interval history is sparse or correlation is weak. Verify the meter has continuous reads, widen the training window, or change the selected point before you retry.

If MAPE or CVRMSE exceed your threshold, try a different point type or confirm the meter is not duplicated in metrics.

Next steps

Building Energy dashboard

Review forecast charts without leaving the Energy dashboard.

Create an M&V model

Configure baselines and savings models alongside forecasting.

M&V analysis

Interpret savings and accuracy metrics after each model run completes.

Energy modeling overview

See how M&V, forecasting, and IPMVP practices fit together in EnerG.

Get Started

Concepts

Portfolio view

Building view

Reference

Open Forecast

Manage configurations