Business analytics case study analysing residential energy consumption to support renewable energy strategy and demand forecasting
Tools: Excel | Time-Series Analysis | Regression
Focus: Demand patterns, peak vs off-peak behaviour, renewable usage
Renewable energy providers face increasing pressure to forecast demand accurately, manage peak loads, and encourage greater adoption of renewable energy sources.
This analysis examines residential energy consumption patterns to identify demand drivers and highlight opportunities where data-driven insights can support renewable energy strategy and operational planning.
The objective of this project is to:
- Analyse residential energy consumption patterns
- Identify differences in usage across time and day type
- Estimate the impact of temperature on energy demand using a regression model
- Highlight implications for renewable energy adoption and demand forecasting
The analysis uses a publicly available residential energy consumption dataset covering approximately 1,000 households.
The dataset includes:
- Time-stamped energy consumption
- Renewable energy usage
- Environmental factors (temperature, humidity)
- Contextual indicators (day type, peak vs off-peak periods)
The data represents a short time window (January–February) and is used to explore consumption patterns rather than long-term seasonality. Data source: Public Kaggle dataset on residential energy consumption (https://www.kaggle.com/datasets/mrsimple07/energy-consumption-prediction/data)
The analysis was carried out using a structured analytics workflow:
- Data cleaning and preparation to ensure consistency and usability
- Descriptive analysis to identify consumption patterns and behavioural differences
- Comparative analysis across weekdays, weekends, and peak vs off-peak periods
- Correlation analysis between temperature and energy consumption
- Linear regression modelling to estimate temperature-driven demand
The focus was on clarity, interpretability, and business relevance rather than model complexity.
Analytical steps were guided by business questions around demand drivers, renewable energy adoption, and peak-load management
- Renewable energy accounted for approximately 20% of total household energy consumption, which shows significant potential to increase adoption.
- Energy consumption was consistently higher during weekends and holidays compared to weekdays, reflecting increased residential demand
- Average household energy consumption is only slightly higher during peak hours compared to off-peak periods, indicating that peak-load pressures are not driven by sustained increases in average daily usage
- Renewable energy usage remains consistently low and largely unchanged across both peak and off-peak hours, suggesting limited alignment between renewable supply and periods of highest demand
- Temperature showed a moderately strong positive relationship with energy consumption, explaining approximately 48.5% of demand variation
- Energy demand increased by approximately 2 units for every 1°C rise, which indicates that temperature is a meaningful short-term demand driver.
- Renewable energy currently represents ~20% of total household consumption, which shows a substantial adoption gap and a clear opportunity to expand renewable penetration through targeted offerings
- Peak and weekend demand management should focus less on average consumption differences and more on short-duration demand spikes, suggesting that time-of-use pricing and load-shifting incentives may be more effective than capacity-led solutions.
- Temperature-driven demand explains approximately 48.5% of observed variation, which supports the use of temperature-based forecasting for short-term operational planning and capacity management.
- An approximate increase of 2 energy units per 1°C rise provides a practical forecasting input that can be embedded into operational planning and demand-response strategies.
- Short-term consumption insights from smart-meter and time-of-use data can inform longer-term investment and infrastructure planning for renewable energy assets by aligning supply expansion with demand drivers.
- Microsoft Excel
- Data cleaning and preparation
- Pivot-table based descriptive analysis
- Linear regression using Analysis ToolPak
- Visual analysis via charts
This project is presented as a professional analytics case study, focused on business insight, decision support, and clear communication of findings using structured analytical methods and real-world datasets.