Retail Sector Bottom-Up Demand Type Profiling & Seasonal Forecasting

Data Transparency & Forecasting MethodologyThis

case study uses a real-world historical energy dataset that has been anonymized and generalized for publication. The original customer segment, location, dates, and other potentially identifying information have been replaced or represented as Sample Town. The dataset is preserved as a fixed historical reference rather than presented as a source of current market data.Historical electricity consumption remains useful for studying recurring demand characteristics such as daily and weekly cycles, seasonal changes, weather-driven variations, operating schedules, and differences between consumer profiles. Maintaining the original historical behavior also allows forecasting results to be evaluated against known observations in a consistent environment.

The analytical technology applied to this historical dataset is not limited to the technology available when the data was collected. The study uses the latest applicable AI and machine-learning capabilities available as of 2026, delivered through QuantRisk’s QR ALPS (AI Load Prediction System).

The workflow combines modern data analysis, feature engineering, AI/ML model configuration, automated optimization, model training, forecasting, and accuracy evaluation. Consequently, the case study illustrates the application of modern AI to authentic historical energy behavior, while making no claim that the historical dataset represents current consumption conditions.

1. Business Challenge

Retail electricity demand has a distinctive operating pattern. Consumption is closely connected to store opening hours, customer activity, staffing levels, building systems, weather conditions, and weekly operating schedules.

Unlike facilities that operate continuously, retail locations can experience substantial differences between overnight, daytime, weekday, Saturday, and Sunday consumption. Seasonal changes can also shift both the timing and magnitude of demand.

This case study explores the development of an AI-powered load forecasting solution for retail stores in a Sample Town. The objective was to generate hourly electricity consumption forecasts for the final week of each season, using historical demand patterns together with calendar and environmental variables.

The exercise was designed as a historical, one-time forecasting study. The final week of each seasonal period was withheld from the modeling dataset and treated as the forecast horizon. Earlier observations were used for exploratory analysis, feature development, model experimentation, and validation.

The forecasting workflow was implemented using QR ALPS, providing an integrated environment for data preparation, feature engineering, machine-learning model configuration, automated optimization, forecasting, and performance analysis.

2. Data Foundation

The forecasting process was built around a proprietary and anonymized historical hourly electricity consumption dataset representing retail stores in Sample Town.

For this case study, the historical dataset was frozen and used without subsequent revisions. The last week of each seasonal period was reserved for the final forecast, while the preceding observations were available for model development.

Data Sources:

The forecasting workflow incorporated:

• Historical retail electricity consumption as the target time series.
• Hourly regional weather observations, including temperature and relative humidity.
• Solar radiation measurements, including DHI, DNI, and GHI, evaluated as potential predictors.
• Calendar information representing working days, weekends, and relevant holidays.
• Time-based variables such as hour of day, weekday, and week number.

The primary forecast output is:

• Hourly retail electricity load
• The framework can also be extended beyond a single expected-value forecast to provide probabilistic forecasts and uncertainty intervals.

3. Exploring the Retail Load Profile

The first stage of the forecasting process was to understand how electricity consumption behaves across different operating periods.

For retail facilities, demand is not driven solely by the passage of time. Store operations, customer presence, lighting, HVAC systems, equipment, and closing schedules can all influence the shape of the load curve.

The exploratory analysis therefore examined both seasonal behavior and day-of-week differences.

For analytical purposes, the year was divided into:

• Spring: March 1 – May 31
• Summer: June 1 – August 31
• Autumn: September 1 – November 30
• Winter: December 1 – February 28

The final week of each season was reserved for forecasting.

Historical comparable periods were used to test model configurations before the final model was applied to the unseen forecast interval.

3.1. Spring: Building the Retail Day Profile

Spring data showed a clear transition from relatively low overnight consumption into the main retail operating period.

Demand began increasing at approximately 8:00 AM and remained elevated until around 10:00 PM, after which consumption declined.

The most pronounced demand period occurred between approximately 12:00 PM and 7:00 PM.

Observed Behavior

The analysis identified several useful characteristics:

• A distinct morning ramp-up.
• Sustained consumption through the main retail operating period.
• A broad afternoon and evening peak.
• A significant decline after store activity decreased.
• Strong dependence on the hour of the day.

This made Hour of Day an important predictor for the forecasting model.

Differences Across the Week

The weekday and Saturday profiles were broadly similar, with comparable operating patterns and peak periods.

The principal difference appeared on Sunday.

Sunday showed a lower overall demand level and a somewhat different intraday shape, with the main peak occurring approximately between 12:00 PM and 6:00 PM.

This suggested that the model needed to recognize Sunday as a different operating regime rather than simply treating it as another weekday.

Forecasting Implication

The Spring analysis supported the use of:

• Hour of day
• Weekday
• Historical demand
• Calendar information
• Intraday model segmentation

The Model Splitter was therefore considered useful for separating materially different portions of the retail operating day.

3.2. Summer: Extended Operating Demand

Summer displayed a somewhat earlier start to the retail demand ramp.

Consumption began increasing at approximately 7:00 AM and remained elevated until around 10:00 PM.

The strongest demand period extended from approximately 12:00 PM to 8:00 PM.

Key Observations

The Summer profile demonstrated:

• An early morning increase.
• A sustained daytime operating period.
• A broad afternoon/evening peak.
• A gradual decline toward the end of the operating day.
• Strong hourly dependence.

The combination of store activity and weather conditions made Summer particularly relevant for evaluating external predictors such as temperature and humidity.

Weekly Operating Pattern

Weekdays and Saturdays showed broadly comparable load structures.

The main demand period on these days extended from approximately 9:00 AM through 8:00 PM.

Sunday again displayed a lower overall consumption level and a narrower peak window, with the principal demand period occurring around 12:00 PM to 5:00 PM.

This distinction reinforced the importance of calendar-aware forecasting.

Modeling Implication

The Summer model therefore incorporated:

• Hour of day
• Weekday
• Historical load
• Calendar context
• Weather variables
• Solar radiation
• Model segmentation

3.3. Autumn: Consistent Retail Operating Hours

The Autumn profile retained the characteristic retail pattern observed in the preceding seasons.

Electricity demand started increasing around 7:00 AM and remained elevated until approximately 10:00 PM.

The main peak period occurred from approximately 12:00 PM to 7:00 PM.

Intraday Structure

The analysis showed:

• A morning increase associated with the beginning of store activity.
• Sustained daytime consumption.
• Strong afternoon demand.
• An evening decline as retail operations wound down.
• A clear hourly dependency.

Weekday Versus Weekend

Weekdays and Saturdays maintained broadly similar consumption patterns, with their primary peak extending from approximately 9:00 AM to 7:00 PM.

Sundays exhibited a lower consumption profile and a narrower peak centered around the afternoon.

This indicates that the retail demand profile contains multiple operating states that should be represented explicitly within the forecasting model.

Modeling Implication

The Autumn analysis again supported:

• Hour-of-day features
• Weekday indicators
• Historical lagged demand
• Calendar information
• Specialized intraday models

3.4. Winter: Seasonal Changes in Store Demand

Winter maintained the broad retail operating pattern seen during the other seasons.

Consumption began increasing around 7:00 AM and remained elevated until approximately 10:00 PM.

The primary demand period occurred between approximately 12:00 PM and 7:00 PM.

Weekday Characteristics

Weekdays followed broadly consistent operating profiles.

Monday showed a somewhat lower demand level than the other weekdays, although the timing of the main peak and off-peak periods remained similar.

Saturday and Sunday

Saturday showed a different profile shape from the weekdays while maintaining a similar overall level to the lower-demand weekday profile.

Sunday remained distinct, with lower consumption and a shorter peak period concentrated approximately between 12:00 PM and 5:00 PM.

These differences demonstrate the value of explicitly modeling day-of-week effects.

Modeling Implication

For Winter, the model continued to use:

• Hour
• Weekday
• Historical demand
• Week-based predictors
• Calendar information
• External environmental variables
• Model segmentation

4. Identifying External Demand Drivers

Historical load alone does not explain every change in retail electricity consumption.

The next stage of the analysis examined external time series to determine whether weather and environmental conditions could provide additional forecasting information.

The variables evaluated included:

• Temperature
• Solar radiation
• Relative humidity

Correlation and behavioral analysis were used to determine whether these variables added meaningful information to the retail load profile.

4.1. Temperature and Retail Energy Demand

Temperature can influence retail electricity consumption through heating, cooling, ventilation, and other building-energy requirements.

The analysis found a relationship between temperature and retail electricity demand across the seasonal periods.

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

In the historical dataset, higher-temperature periods were associated with higher consumption levels in several parts of the year. The relationship also varied according to the time of day.

This makes temperature useful not only as a seasonal indicator but also as a variable capable of helping the model adjust its expected load during specific operating periods.

Modeling Decision

Temperature was incorporated as an external predictor.

Its role is to help the AI model adapt the expected retail load to changing environmental conditions rather than relying entirely on historical demand patterns.

4.2. Relative Humidity

Relative humidity was also investigated as a potential explanatory variable.

The historical data indicated an inverse relationship in parts of the observed retail load profile, with reductions in humidity corresponding to higher consumption during certain periods.

Because humidity can interact with temperature and building cooling requirements, it can provide additional information about environmental conditions.

Modeling Decision

Relative humidity was included as an external predictor to allow the forecasting model to account for this additional environmental signal.

5. AI-Powered Retail Load Forecasting

Following the exploratory analysis, the forecasting architecture was implemented using QR ALPS.

The platform provides a no-code drag and drop environment for assembling data-processing, feature-engineering, machine-learning, and forecasting components.

Season-specific models were developed because retail consumption changed across the year.

5.1. Data Preparation and Quality Controls

Before training the AI models, the historical time series was prepared for forecasting.

Handling Data Gaps

QR ALPS supports several approaches for addressing missing observations, such as Linear interpolation, Daily-pattern filling, Weekly-pattern filling etc.

Calendar Processing

Calendar information was particularly important for retail demand forecasting.

The model was configured to distinguish:

• Weekdays
• Saturdays
• Sundays
• Holidays
• Other calendar-specific conditions

This allows the model to recognize that the same hour can correspond to very different demand levels depending on the operating day.

5.2. Retail Load Feature Engineering

The feature set was constructed from the behavioral patterns identified during the analysis.

Additional moving-average configurations could also be evaluated depending on the seasonal model.

[QR Forecaster Feature Engineering Dashboard Load Forecast Model for Retail Consumer Type for Spring]

The resulting feature architecture combines four complementary information sources:

• Recent Demand + Weekly Recurrence + Calendar Context + Environmental Conditions
• This provides the machine-learning model with a broader representation of the factors influencing retail electricity demand.

5.3. Segmenting Different Retail Operating Periods

One of the important characteristics of the retail dataset is the difference between overnight and operating-hour consumption.

Rather than requiring one model to learn all parts of the daily curve equally, the Model Splitter was configured to create specialized forecasting models for different periods of the day.

The case-study configuration used three segments:

• Midnight – 4:00 AM
• 4:00 AM – 3:00 PM
• 3:00 PM – End of Day

This approach allows individual models to focus on different demand regimes.

The segmentation boundaries are configuration choices and can be changed when model validation demonstrates that alternative divisions better represent a particular retail profile.

5.4. Machine-Learning Model Evaluation

Several machine-learning algorithms were evaluated as part of the model-development process, including:

• NGBoost
• LightGBM
• XGBoost

These algorithms can capture nonlinear relationships between historical consumption, calendar variables, and external environmental predictors.

For example, XGBoost exposes a range of hyperparameters that can materially affect forecasting behavior, including tree depth, estimators, subsampling, regularization, objective functions, and evaluation criteria.

Manually evaluating all possible combinations quickly becomes impractical.

QR ALPS‘s automated optimization functionality was therefore used to systematically evaluate candidate configurations and fine-tune the model parameters.

The original workflow was capable of evaluating the equivalent of thousands of configurations under its tested setup. This is a case-specific observation and should not be interpreted as a guaranteed runtime for every dataset or deployment.

5.5. Training and Forecast Generation

After model selection and optimization, the finalized models were trained on the historical seasonal data.

Each seasonal configuration used approximately three months of hourly observations for model development.

The original case-study workflow reported that training one of the selected machine-learning models and executing the forecast required approximately one minute.

Actual execution time will vary depending on data volume, model architecture, number of predictors, optimization settings, and computing resources.

6. Forecast Evaluation and Visualization

The final forecasting stage compares model predictions against known historical observations and generates forecasts for the reserved periods.

QR ALPS provides interactive visualization capabilities for examining. These views allow users to move beyond a single accuracy number and investigate where forecasting performance changes across the day or across different operating periods.

6.1. Historical Forecast Validation

The validation process was structured around the seasonal forecast horizon.

The final week of each season was withheld from the model-development dataset.

Earlier periods were used to experiment with alternative feature combinations, model configurations, and segmentation strategies.

Once the preferred configuration was selected, it was applied to the unseen seasonal forecast periods.

This provides a historical simulation of how the model would perform when asked to forecast a future week without access to its actual demand.

6.2. Accuracy Results

The validated error figures for each season are presented below:

Spring

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

Summer

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

Autumn

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

Winter

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

The results should be interpreted specifically within the context of the Sample Town dataset, forecast horizon, selected predictors, and model configuration rather than being presented as representative accuracy for all retail businesses.

7. From Historical Analysis to Operational Forecasting

Although this case study uses a frozen historical dataset, the same architecture can support a production forecasting environment.

A continuously operating retail forecasting workflow could connect:

• Retail Meter Data → Data Hub → Data Quality → External Data → Feature Engineering → AI Forecast → Monitoring → Live Dashboard
• New meter observations can be incorporated as they become available, while forecast errors can be monitored against subsequent actual consumption.
• Weather forecasts can also be incorporated as future predictors, allowing the load model to adapt to expected environmental conditions.
• This transforms the forecasting process from a one-time analytical exercise into a continuously monitored energy intelligence workflow.

8. Probabilistic Retail Load Forecasting

A single forecast value does not communicate the full range of possible future demand.

For retail energy planning, a probabilistic forecasting approach can provide an expected load together with uncertainty intervals or forecast distributions.

This can help energy and operations teams evaluate questions such as:

• What is the expected store demand?
• How high could demand reasonably become?
• Which hours have the greatest uncertainty?
• How much demand-side flexibility may be required?
• What level of demand should be considered for risk-aware planning?

Probabilistic forecasting is particularly useful when uncertainty needs to be incorporated into operational or financial decisions rather than relying exclusively on a deterministic forecast.

9. Continuous Monitoring and Interactive Dashboards

A production implementation can extend the forecasting workflow beyond model training.

Interactive dashboards can bring together:

• Current and future load forecasts
• Historical actual consumption
• Forecast errors
• Model-performance indicators
• Seasonal trends
• Uncertainty intervals
• Scenario results

Continuous monitoring can also help identify changes in the underlying demand pattern.

For example, a persistent deterioration in forecast performance could indicate a change in store operating behavior, building usage, weather sensitivity, or another factor that warrants investigation.

This creates a feedback loop between forecasting, monitoring, and model improvement.

10. Learn More About AI Electricity Load Forecasting

For additional information about QR ALPS, including AI-powered electricity load 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.