Multifamily Residential Electricity Load Forecasting: Sample Town Case Study

Data & Case Study Disclaimer

This case study is based on proprietary, historical, and anonymized energy data used for analytical and demonstration purposes. The location, organization, dates, and other identifying characteristics have been generalized and are presented as Sample Town. The dataset is a frozen historical dataset and does not represent a live customer environment. Although the underlying observations are historical, the analysis applies current AI and machine-learning technology available as of 2026 through QuantRisk’s QR ALPS (AI Load Prediction System). The forecasting workflow uses modern machine-learning libraries and techniques for feature engineering, model configuration, optimization, training, and forecast evaluation.

1. The Forecasting Challenge

Electricity demand in multifamily and residential buildings is fundamentally different from that of commercial facilities. Instead of following a fixed business schedule, residential consumption reflects the collective behavior of occupants. Morning routines, departures, daytime occupancy, evening activity, weekends, weather conditions, and seasonal heating or cooling requirements can all influence the aggregate load.

This case study examines the development of an AI-powered hourly electricity load forecasting model for multifamily residential consumption in a Sample Town. The objective was to forecast the final week of each season using historical electricity demand together with temporal, calendar, and environmental information.

The exercise was structured as a historical forecasting study. For each season, the final week was withheld from model development and treated as an unseen forecast horizon. Previous comparable periods were used for exploratory analysis, feature engineering, model experimentation, and validation.

The forecasting workflow was implemented with QR ALPS, providing an integrated environment for data preparation, feature engineering, machine-learning model optimization, forecast generation, and accuracy analysis.

2. Data Foundation

The analysis uses a proprietary and anonymized historical hourly electricity consumption dataset representing multifamily residential demand in Sample Town.

The forecasting workflow considered:

• Historical hourly multifamily electricity consumption
• Hourly regional weather observations
• Temperature
• Relative humidity
• Solar radiation measurements
• US calendar information
• Hour-of-day and weekday information

The principal forecasting target is:

Hourly multifamily/residential electricity load

The architecture can additionally support probabilistic forecasts and uncertainty intervals when these are required for risk-aware planning and decision-making.

3. Understanding Residential Demand Behavior

Before selecting a forecasting algorithm, the data science process focused on understanding the underlying consumption behavior. Multifamily demand contains recurring patterns, but those patterns are not identical across all days. Residential occupants tend to create characteristic morning and evening demand periods, while weekends can exhibit a noticeably different daytime profile.

The analysis therefore examined the historical load at several levels:

• Seasonal behavior
• Hourly demand patterns
• Weekday versus weekend profiles
• Environmental relationships
• Changes between different portions of the day

For analytical purposes, the historical observations were separated into four seasonal periods:

• Spring
• Summer
• Autumn
• Winter

[Raw Data for The Whole Year – Multifamily]

3.1. Spring: Two Distinct Residential Peaks

Spring exhibited a characteristic two-peak residential demand curve.

[Raw data behavior of Spring data for Multifamily]

The plot below illustrates the average hourly electricity consumption during the three-month spring training period, from March 1 to May 24. The analysis highlights the typical daily consumption profile, including peak and off-peak periods.

The first major increase occurred in the morning, with demand reaching a peak around 7:00 AM. Consumption then declined before building again during the evening, with the second major peak appearing around 8:00 PM.

The lowest demand occurred during the early morning hours, particularly around 3:00 AM.

[Hourly Average Behavior of Spring Data for Multifamily]

This shape is consistent with a residential operating pattern in which occupants are active during morning routines, demand falls during part of the daytime, and electricity use rises again as residents return home later in the day.

Weekday Profiles

The weekday profiles were broadly consistent in shape but varied somewhat in overall level.

[Daily Average Behavior of Spring Data for Multifamily]

Two principal weekday peaks were observed:

• Morning: approximately 7:00 AM
• Evening: approximately 8:00 PM

However, the weekend profile was different. The morning peak shifted later, toward approximately 10:00 AM, while the evening peak remained close to the weekday pattern.

Another important distinction was the higher level of daytime consumption during weekends compared with weekdays. This difference is particularly relevant for multifamily forecasting because residents are more likely to remain at home during weekend daytime hours.

Forecasting Implications

The Spring analysis indicated that the model should explicitly represent:

• Hour of day
• Weekday/weekend status
• Historical daily behavior
• Weekly recurrence

The QR ALPS Model Splitter was therefore configured to distinguish between weekdays, Saturdays, and Sundays, allowing separate models to learn these different operating profiles.

3.2. Summer: Stronger Evening Demand

Summer introduced a noticeable change in the residential load shape.

[Raw data behavior of Summer data for Multifamily]

The chart below presents average hourly consumption based on the summer training dataset covering June 1 through August 24. It provides insight into recurring daily load patterns and highlights the hours of peak and off-peak consumption.

The morning peak that was prominent in Spring became less pronounced, while evening consumption became comparatively stronger. The highest average demand occurred around 8:00 PM. Following the evening peak, consumption declined through the night and reached its lowest level around 4:00 AM. A gradual increase began during the early morning, followed by a relatively stable daytime level before demand increased again toward the evening peak.

[Hourly Average Behavior of Summer Data for Multifamily]

Weekly Profiles

Weekdays maintained a broadly similar structure, although consumption levels varied across the afternoon.

The two main demand periods were:

• Morning: approximately 7:00 AM
• Evening: approximately 8:00 or 9:00 PM

During weekends, the morning activity shifted considerably later, reaching approximately 11:00 AM to noon. The evening peak remained present, while Sunday exhibited somewhat higher consumption during the second peak than the other days. As in Spring, weekend daytime consumption remained noticeably higher than the weekday daytime profile.

[Daily Average Behavior of Summer Data for Multifamily]

Forecasting Implications

The Summer data reinforced the importance of combining:

• Hour-of-day information
• Day-of-week classification
• Recent load history
• Weekly lagged demand
• Seasonal information
• Weather variables

Rather than assuming that one generic daily profile would represent the entire week, the forecasting architecture allowed different models to capture the distinct weekday and weekend behaviors.

3.3. Autumn: Return of the Two-Peak Profile

The Autumn load pattern moved closer to the structure observed during Spring.

[Raw data behavior of Autumn data for Multifamily]

The following plot presents the average hourly consumption observed during the autumn training period, spanning September 1 to November 23. The analysis helps identify intraday consumption trends and distinguish higher-demand periods from off-peak hours.

Two major consumption periods were visible:

• Morning: approximately 7:00 AM
• Evening: approximately 8:00 PM

Demand reached its lowest point around 4:00 AM, followed by a gradual morning increase. Overall, the Autumn profile was similar to Spring, although the first morning peak was somewhat lower.

[Hourly Average Behavior of Autumn Data for Multifamily]

Weekday Profiles

Weekdays continued to exhibit a relatively stable two-peak structure. The main morning peak occurred around 7:00 AM, while the evening demand period extended approximately from 7:00 PM to 9:00 PM.

Weekend demand differed in both timing and level. The Saturday morning peak shifted toward approximately 10:00 AM, while Sunday reached its morning peak closer to noon. The evening peak remained broadly aligned with the weekday evening period. Daytime consumption was again higher on weekends than on weekdays, providing a strong indication that weekday/weekend classification should be part of the model configuration.

[Daily Average Behavior of Autumn Data for Multifamily]

Forecasting Implications

The Autumn analysis supported the continued use of:

• Hour
• Weekday
• Previous-day demand
• Previous-week demand
• Seasonal context
• Separate weekday/Saturday/Sunday modeling

3.4. Winter: Morning and Evening Residential Peaks

[Raw data behavior of Winter data for Multifamily]

Winter retained the fundamental residential two-peak structure. The first peak occurred around 7:00 AM, followed by a reduction in consumption during the middle portion of the day. A second and generally stronger residential activity period appeared in the evening around 8:00 PM. The lowest consumption remained concentrated around 3:00 AM. Another lower-demand period appeared during approximately 2:00 PM, after which demand began increasing toward the evening peak.

The chart below summarizes the hourly average consumption for the winter period, using training data from January and February together with December 1-24. The resulting profile highlights the daily demand pattern and helps determine the typical peak and off-peak consumption hours.

[Hourly Average Behavior of Winter Data for Multifamily]

Weekday Profiles

The principal weekday demand periods were:

• Morning: at 7:00 AM
• Evening: approximately 7:00 – 9:00 PM

The weekend morning peak shifted later, occurring around 9:00 AM. The evening peak remained in approximately the same time window as weekdays, but its magnitude was lower. Weekend daytime demand was again higher relative to weekday daytime consumption. These observations demonstrate that multifamily residential demand is strongly dependent on both time of day and day type.

4. External Drivers of Residential Electricity Consumption

Historical demand patterns provide an important foundation for forecasting, but they do not fully explain changes in residential electricity consumption. The next stage of the analysis evaluated external time series to determine which environmental variables could provide additional predictive information.

The analysis focused primarily on:

• Temperature
• Solar radiation

The source analysis identified meaningful relationships between these variables and multifamily electricity demand.

4.1. Temperature as a Seasonal Demand Driver

Temperature showed a different relationship with multifamily electricity consumption depending on the season. In Summer, higher temperatures were associated with increased electricity demand, consistent with greater cooling requirements. In Winter, lower temperatures were also associated with elevated consumption, consistent with increased heating requirements. This means that temperature cannot necessarily be treated as having one fixed relationship with load throughout the year. Instead, its influence can vary according to the seasonal operating regime.

[Average Behavior of Multifamily Electricity Consumption vs Temperature Separated by Seasons]

Modeling Decision

Temperature was therefore included as an external predictor, with seasonal model configurations allowing the relationship between weather and demand to be captured appropriately. This provides the forecasting model with information that historical load alone cannot provide, particularly when future weather conditions differ from the historical average.

4.2. Solar Radiation and Residential Activity

Solar radiation was also evaluated using indicators such as:

• DHI
• DNI
• GHI

The analysis showed that solar radiation provides a useful representation of the transition between nighttime and daytime conditions.

[Hourly Average Behavior of Multifamily Data vs Temperature and Solar Radiance Separated by Seasons]

The relationship with residential electricity consumption is particularly interesting because multifamily demand tends to increase during periods when residents are at home, including the early morning and evening. Solar radiation therefore provides an additional temporal signal that complements the hour-of-day predictor. The source analysis also identified an inverse relationship between the solar-radiation profile and portions of the residential consumption curve.

Modeling Decision

Solar radiation was incorporated as an external predictor to help the model distinguish different portions of the daily multifamily residential load profile.

5. Building the AI Forecasting Model

Once the behavioral and correlation analysis was complete, the forecasting architecture was configured in QR ALPS. Rather than relying on a single generic forecasting algorithm, the approach combines data preparation, engineered predictors, model segmentation, automated model optimization, and seasonal training.

Separate seasonal configurations were developed because multifamily residential electricity demand changes materially throughout the year. The overall process can be represented as:

Historical Data → Behavioral Analysis → Feature Engineering → Model Segmentation → AI Optimization → Forecasting → Validation

5.1. Preparing the Train Data

Data preparation was performed before the machine-learning models were trained.

Gap Management

QR ALPS provides multiple approaches for addressing missing observations, including:

• Linear interpolation
• Daily-pattern methods
• Weekly-pattern methods

The case-study dataset did not generally contain significant missing observations. Nevertheless, gap-filling functionality was included as part of the broader data-quality workflow.

Calendar Processing

Calendar information was used to distinguish different day types and provide the model with additional temporal context. The forecasting system can recognize:

• Working days
• Saturdays
• Sundays
• Holidays

This is particularly important for multifamily demand because residential consumption does not follow the same weekday/weekend relationship as many commercial facilities.

5.2. Feature Engineering for Multifamily Load

The feature set was designed around the patterns identified during exploratory analysis.

Simple or exponential moving averages could also be tested where they provided additional predictive value. The objective was to combine short-term demand persistence with recurring daily and weekly behavior and external environmental information.

QR ALPS Feature Engineering Dashboard, Load Forecast Model for Multifamily Consumer Type for Spring

5.3. Modeling Different Parts of the Residential Day

The multifamily load curve contains several distinct behavioral transitions:

• Overnight minimum
• Morning ramp
• Morning peak
• Daytime decline/stability
• Afternoon transition
• Evening ramp
• Evening peak
• Nighttime decline

Attempting to model all of these regimes with a single model can make the learning problem unnecessarily broad. The Model Splitter was therefore configured to divide the day into five forecasting segments:

1. 12:00 AM – 4:00 AM
2. 4:00 AM – 7:00 AM
3. 7:00 AM – 2:00 PM
4. 2:00 PM – 8:00 PM
5. 8:00 PM – 12:00 AM

This allows separate models to focus on periods with different upward, downward, and peak-demand behavior. The segmentation is configurable and can be adjusted when validation indicates that a different set of boundaries better represents a particular residential portfolio.

5.4. AI Model Selection and Automated Optimization

Several machine-learning approaches were evaluated within the forecasting workflow. These models are capable of representing nonlinear interactions among historical demand, time variables, weather conditions, and other predictors. XGBoost, for example, exposes numerous configuration parameters controlling tree structure, regularization, sampling, objective functions, and evaluation behavior. Testing these parameters manually quickly becomes impractical.

The automated optimization capabilities of QR ALPS were therefore used to evaluate candidate configurations and fine-tune the selected models. The underlying workflow can evaluate a large number of model configurations systematically, helping reduce the risk of relying on an arbitrarily selected parameter set.

5.5. Model Training and Forecast Execution

After model configuration and optimization, the selected models were trained using the historical observations available for each seasonal period. Each seasonal model used approximately three months of hourly historical data. The original source analysis reported a forecast execution time of approximately one minute for one of the tested machine-learning configurations. Actual runtime depends on factors including dataset size, model architecture, number of predictors, optimization settings, and available computing resources.

6. Forecast Validation and Accuracy Analysis

The final stage of the case study evaluates how well the AI models reproduce known residential demand patterns and how they perform when forecasting an unseen period.

QR ALPS provides interactive visualization capabilities for comparing:

• Actual consumption
• Forecast consumption
• Point-level forecast errors
• MAPE
• MAE

The validation methodology follows the structure of the case study. In the accuracy analysis dashboards below you can find the results of our forecasts for the previous to last week of each season when we have actual data to compute the errors plus the last week of each season when there is no actual data and the goal of the case study is to forecast that interval.

Spring forecast dashboard

QR ALPS Data Visualization Dashboard, Actual, Forecast and Accuracy Hourly Data. May 18th – May 31st.

Summer forecast dashboard

QR ALPS Data Visualization Dashboard, Actual, Forecast and Accuracy Hourly Data. Aug 18th – Aug 31st.

Autumn forecast dashboard

QR ALPS Data Visualization Dashboard, Actual, Forecast and Accuracy Hourly Data. Nov. 17th – Nov. 30th.

Winter forecast dashboard

QR ALPS Data Visualization Dashboard, Actual, Forecast and Accuracy Hourly Data. Dec.18th – Dec 31st.

7. Why Multifamily Load Forecasting Requires a Different Approach

Multifamily electricity demand presents a distinctive forecasting problem. The aggregate load reflects the combined behavior of many individual households. This creates recurring patterns, but also introduces variability that is not necessarily visible in a simple commercial operating schedule.

The case analysis identified several characteristics that are particularly important. These characteristics explain why a forecasting architecture designed for an office, school, hotel, or retail facility should not simply be transferred to a multifamily portfolio without recalibration.

8. Probabilistic Forecasting and Uncertainty

For multifamily energy planning, the expected load alone may not be sufficient. A probabilistic load forecast can provide an expected demand trajectory together with uncertainty ranges. This allows energy managers and portfolio operators to consider questions such as:

• What is the expected aggregate residential load?
• What range of demand is reasonably possible?
• Which hours have the greatest forecast uncertainty?
• How much additional capacity may be required?
• Which periods present greater operational or energy-market risk?

Uncertainty information can therefore complement deterministic forecasts when decisions need to account for the range of possible future demand.

9. Scenario Simulation

The forecasting architecture can also support what-if analysis. Rather than evaluating only one expected future, users can simulate alternative conditions affecting residential electricity demand. Examples include:

• Warmer or cooler weather
• Alternative temperature trajectories
• Different solar conditions
• Changes in seasonal conditions
• Changes in operating or occupancy assumptions where applicable

Scenario analysis can help portfolio managers evaluate how expected electricity demand could change under different assumptions.

10. Monitoring and Interactive Forecast Dashboards

A production deployment can provide continuous visibility into both the forecast and its performance. Interactive dashboards can consolidate:

• Forecast load
• Actual consumption
• Historical trends
• Forecast errors
• Seasonal performance
• Uncertainty intervals
• Scenario forecasts
• Model-performance indicators

For additional information about QR ALPS , including AI-powered electricity price forecasting solutions, technical capabilities, case studies, and live interactive forecast dashboards, visit:

https://quantrisk.com/analytics-electricity-load-forecast/

If you have questions about our forecasting technology or would like to discuss your electricity price forecasting requirements, please contact [email protected]. Our team will be pleased to discuss your market, forecasting objectives, and potential applications of probabilistic AI forecasting.