# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 2 Materials and Methods
### Requirements Toward the GAN-Based Synthetic Data for Electrical Load Profiles  
The effectiveness of GAN-generated synthetic data in the domain of electrical load profiling relies on how well it can emulate the statistical, temporal, and behavioral nuances of real-world energy consumption. Unlike image generation, a domain in which GANs excel, electrical load data are deeply tied to daily operational rhythms, seasonal changes, and infrastructure characteristics. Therefore, ensuring that synthetic data mirror these realities is a requirement. To achieve this, the following requirements should be met:  
1. Synthetic load profiles must preserve the overall shape and behavioral dynamics of real electricity consumption. This includes accurately capturing daily cycles, such as morning and evening peaks that occur in most residential buildings, as well as base load behavior during off-peak hours. For industrial facilities, the profiles may follow production-related patterns. If such patterns are not captured, the synthetic data will lack ecological validity and fail to serve its intended purpose.
2. The generated data must respect the statistical properties of the original dataset. This may include preserving the mean, variance, skewness, kurtosis, and other domain-specific features, such as load factor, energy-to-peak ratio, or hour-to-hour correlation coefficients. These properties guarantee how well synthetic data can be used to train or test load forecasting models or simulate grid scenarios. If statistical alignment is not maintained, predictive models trained on such data may produce biased or unstable results, especially in applications like smart grid control or demand response planning.
3. It is critical to maintain the temporal structure of the data. Electrical load profiles are a type of time series, and the value at a given hour (e.g., 3 PM) is not independent of the preceding hours (e.g., 2 PM or 1 PM). Thus, synthetic data must preserve short-term dependencies (e.g., peaks) and longer-term patterns (e.g., weekday/weekend).
4. GANs should not generate a narrow subset of repetitive or overly similar load profiles. In reality, energy consumption varies widely across users due to differences in building type, occupancy patterns, weather sensitivity, appliance usage, and behavior. A household with young children will have a very different load profile than a single-person apartment or an industrial facility. Behavioral variety must be represented in the synthetic data to ensure its usefulness across multiple analysis contexts. Moreover, the model should also be capable of capturing and reproducing rare but important patterns, such as those resulting from heatwaves, holiday periods, or abnormal grid events. Ignoring such anomalies would make the synthetic data unrealistic.
5. Electricity consumption profiles can be highly sensitive and revealing. They can expose personal routines, such as when occupants wake up, leave for work, return home, or go on vacation. A well-trained GAN must generate synthetic profiles that are statistically similar but not identical to any profile in the training set. This helps mitigate the risk of data leakage or re-identification, especially in residential datasets. Techniques such as differential privacy [30], regularization, and architectural constraints can help ensure that the generator learns patterns without memorizing individual examples.