Consumer load profiling involves examining patterns of energy consumption using available data. With smart meter data available at (sub-)hourly intervals, it is possible to use the it to generate daily load profiles that capture the typical consumption behavior across a representative day. Previous work has shown how fine-grained smart meter data and monthly consumption data separated by time-of-use can be translated into representative load profiles for a given group of consumers. In this work, an approach for load profile generation is proposed which only uses monthly energy consumption data without breaking it down further based on time-of-use such as peak, off-peak and mid-peak hours. Performance of data-driven models using random forest, XGboost and multi-layer perceptron is studied by reconstructing the load profiles on a known smart meter dataset. A comprehensive assessment of how the shape of the reconstructed load profile compares with the actual profile is provided by evaluating amplitude errors, time shifts and slope variations. Our investigations provide insights on the role of the cluster size on load profile reconstruction: predictions of profiles for large cluster sizes show improved accuracy in terms root mean-square errors and less bias. By gaining insights into when and how energy is consumed, utilities can implement measures to reduce costs, mitigate peak demand and enhance overall energy efficiency.