This paper proposes and compares feature construction and calibration methods for clustering daily electricity load curves. Such load curves describe electricity demand over a period of time. A rich body of the literature has studied clustering of load curves, usually using temporal features. This limits the potential to discover new knowledge, which may not be best represented as models consisting of all time points on load curves. This paper presents three new methods to construct features: 1) conditional filters on time-resolution-based features; 2) calibration and normalization; and 3) using profile errors. These new features extend the potential of clustering load curves. Moreover, smart metering is now generating high-resolution time series, and so the dimensionality reduction offered by these features is welcome. The clustering results using the proposed new features are compared with clusterings obtained from temporal features, as well as clusterings with Fourier features, using household electricity consumption time series as test data. The experimental results suggest that the proposed feature construction methods offer new means for gaining insight in energy-consumption patterns.