Forecasting

Introduction to Forecasting

Forecasting is one of the most practical forms of Machine Learning in smart buildings. It is used to estimate future values based on historical data and relevant context. In building operations, forecasting helps teams move from reacting to events toward anticipating them. This supports better planning, earlier intervention, and more efficient building performance.

In ABB Ability™ BuildingPro Suites, forecasting fits naturally into the platform vision. BuildingPro Suites connects building automation, Heating, Ventilation, and Air Conditioning (HVAC), energy, Information Technology (IT), Operational Technology (OT), and other systems to transform siloed building data into actionable insights, predictive tools, and intelligence [1]. Forecasting builds on this foundation by turning time series data into useful forward-looking information.

This page explains what forecasting is, why it matters in the smart building context, and how forecasting is used in BuildingPro Suites, with a particular focus on energy forecasting.

What is Forecasting?

Forecasting is the prediction of future values based on historical observations. In a building context, this usually means taking a sequence of measured values over time and estimating what is likely to happen next.

A forecast can be short-term or long-term. It can be used to predict the next few minutes, the next day, or the next week, depending on the use case. The target can be energy consumption, room temperature, occupancy-related behavior, equipment load, or another time-dependent signal.

The purpose of forecasting is not only to produce a future value. Its real value lies in helping users and systems make better decisions. A good forecast provides a view of what is likely to happen so that building teams can act before issues become visible in alarms, costs, or comfort complaints.

Why is Forecasting needed in the Smart Building context?

Forecasting is especially useful in smart buildings because many operational decisions depend on what is expected to happen next, not only on what is happening now.

Buildings are dynamic systems. Energy demand changes during the day. Temperatures react to occupancy, weather, and system settings. Technical systems follow recurring cycles but can also shift when operating conditions change. In large buildings and portfolios, these patterns are too complex to manage with manual review alone.

Forecasting helps answer questions such as:

• How much energy is likely to be consumed in the next hours or days?

• How will room temperature develop under current conditions?

• Is the current system behavior in line with what we should expect?

• When is demand likely to rise or fall?

• Which deviations from expected behavior may signal a problem?

This aligns well with the BuildingPro Suites positioning around predictive tools, smarter data-driven decisions, demand prediction, and proactive AI [1].

Benefits of Forecasting

Forecasting provides several important benefits in the smart building context.

Better planning

Forecasts help users prepare for what is coming next. This is useful for operational planning, energy planning, and portfolio monitoring. Instead of reacting only after a value has changed, teams can work with an expectation of future behavior.

Improved energy efficiency

When future demand can be estimated in advance, energy use can be managed more actively. This helps identify peaks, reduce waste, and support more efficient control strategies.

Earlier anomaly detection

A forecast defines what is expected under current conditions. If the actual value later deviates significantly from the predicted behavior, this difference can be used as an early signal that something unusual is happening.

Better control support

Forecasts are useful not only for human decision-making but also for digital workflows. They can be used in dashboards, comparisons, automation logic, optimization workflows, or as input into further models.

Scalability across assets and portfolios

Forecasting is especially valuable in platforms that connect many systems and sites. A forecasting approach that works across different buildings and assets can support consistent decision-making at scale.

Typical use cases for Forecasting

Forecasting can be applied to many building-related time series. In BuildingPro Suites, the documented forecast use cases already include energy consumption in buildings, room temperatures and climate control, and condition monitoring of systems [2].

Typical forecasting applications include:

• Energy forecasting for electricity, gas, or water consumption

• Temperature forecasting for rooms, zones, or system outputs

• Load forecasting for operational planning and system monitoring

• Condition forecasting for detecting deviations from expected technical behavior

• Portfolio forecasting for comparing future trends across multiple buildings

Among these, energy forecasting is one of the most important and broadly useful applications.

Energy Forecasting

Energy forecasting is a core smart building use case because energy consumption is time-dependent, operationally important, and closely linked to cost, sustainability, and performance.

Building energy demand is influenced by many factors, such as:

• time of day

• day of week

• season

• weather conditions

• occupancy and space usage

• operating schedules

• equipment behavior

Because these drivers interact over time, energy forecasting is a strong fit for Machine Learning. It allows the system to learn recurring patterns and dependencies that would be difficult to describe through static rules alone.

In practice, energy forecasting can be used for:

• short-term energy demand estimation

• early detection of unusual consumption behavior

• better planning of procurement or operational response

• expected-versus-actual comparisons

• support for efficiency and optimization workflows

The BuildingPro Suites forecast documentation already describes the prediction of electricity, gas, or water consumption as a typical use case and notes that feature attributes such as hour of day, day of week, and outdoor temperature can improve the result [2].

Forecasting in BuildingPro Suites

Forecasting in BuildingPro Suites is designed to make time series prediction practical and useful in real building operations. The platform already provides important foundations for this. It integrates data from building automation, HVAC, energy, IT, OT, and third-party systems, and it structures this data through tags, assets, attributes, and points [1][2].

This makes it possible to use forecasting not as an isolated model output, but as part of a broader operational system.

In BuildingPro Suites, forecast outputs can be connected back into the platform and used in dashboards, visualizations, rules, workflows, and other applications [2]. This is important because the value of a forecast is highest when it can be consumed directly in the same environment where users monitor and manage buildings.

Foundation-model-based Forecasting

The forecasting approach used in our current AI and ML work goes beyond classical task-specific time series models.

We use state-of-the-art zero-shot time series foundation models for forecasting. These models are pretrained on very large amounts of building-related time series data and can therefore generalize well across different forecasting tasks and different building contexts.

This changes the way forecasting works in practice.

Traditional forecasting pipelines often require a separate model to be trained for each signal or use case. They also depend heavily on having enough local historical data before they become useful. In contrast, zero-shot time series foundation models start from a much stronger prior. Because they have already learned broad time series structure during pretraining, they can produce highly useful forecasts even when only limited local data is available.

This is especially valuable in smart buildings, where some assets have rich historical data while others may have only short histories, changing conditions, or irregular operational patterns.

Why Foundation Models are useful for forecasting

Foundation-model-based forecasting offers several important advantages.

High quality forecasts with little local data

Because the models are pretrained broadly, they do not need large amounts of task-specific data to become useful. This makes them much more practical for real-world building deployments, especially when a new asset, site, or use case is introduced.

Strong zero-shot generalization

A zero-shot model can produce useful predictions without requiring a dedicated retraining cycle for every new signal. This makes forecasting faster to deploy and easier to scale.

Better adaptation to changing conditions

Building environments change continuously. Occupancy patterns shift. Weather changes. Equipment behavior evolves. A strong forecasting model must remain useful under changing context. Foundation models are especially valuable here because they adapt well and quickly when new data arrives, without depending on frequent retraining of separate local models.

Less operational overhead

If every forecast requires individual model training, maintenance becomes complex and expensive. A foundation-model-based approach reduces this overhead and enables a more scalable forecasting architecture.

The role of context

Forecast quality improves when the model understands more than the target time series alone.

In BuildingPro Suites, forecasting can use contextual features to improve predictive power. Examples of useful context include:

• weather data

• outdoor temperature

• people count

• occupancy-related signals

• system state

• time-based features such as hour of day or day of week

• other operational signals related to the forecast target

This is particularly important in buildings because many target variables are not driven only by their own history. Energy demand depends strongly on weather and occupancy. Room conditions depend on schedules, usage, and system operation. Equipment-related signals often depend on surrounding technical context.

By allowing relevant context to be included, forecasting becomes more accurate and more robust.

The documented Forecast App already supports feature attributes and time-based context, and mentions optional inputs such as outdoor temperature and other asset attributes [2]. The broader forecasting direction extends this principle further by making richer context an essential part of high-quality prediction.

Forecasting and adaptation

One of the key challenges in smart building forecasting is change. A model may perform well under one set of conditions but become less reliable when usage patterns, weather conditions, or system behavior shifts.

For this reason, adaptability is critical.

In our current forecasting approach, the models are designed to adapt well to new incoming data and changing operational context. They do not depend on constant retraining cycles in the way many traditional forecasting systems do. Instead, they remain responsive and useful as new patterns appear, which makes them much better suited to dynamic real-world building environments.

This is an important advantage in smart buildings, where stable conditions cannot be assumed over long periods of time.

Conclusion

Forecasting is a key capability in smart buildings because it helps estimate future behavior and turns time series data into practical operational value. It supports better planning, improved efficiency, earlier anomaly detection, and more proactive decision-making.

In ABB Ability™ BuildingPro Suites, forecasting builds on the platform’s strengths in connected data, structured context, and integrated intelligence [1][2]. It is especially relevant for energy forecasting, where future demand and consumption patterns are central to operational and sustainability goals.

Our current AI and ML direction extends this further through zero-shot time series foundation models. These models are highly useful in the building domain because they provide accurate forecasts with little local data, can incorporate contextual inputs such as weather and people count, and adapt quickly to changing conditions without depending on repeated retraining.

This makes forecasting not only a predictive capability, but an important building block for scalable and practical building intelligence.