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This paper analyses the multi-objective design of an inductor for a DC-DC buck converter. The core volume and total losses are the two competing objectives, which should be minimised while satisfying the design constraints on the required differential inductance profile and the maximum overheating. The multi-objective optimisation problem is solved by means of a population-based metaheuristic algorithm based on Artificial Immune Systems (AIS). Despite its effectiveness in finding the Pareto front, the algorithm requires the evaluation of many candidate solutions before converging. In the case of the inductor design problem, the evaluation of a configuration is time-consuming. In fact, a non-linear iterative technique (fixed point) is needed to obtain the differential inductance profile of the configuration, as it may operate in conditions of partial saturation. However, many configurations evaluated during an optimisation do not comply with the design constraint, resulting in expensive and unnecessary calculations. Therefore, this paper proposes the adoption of a data-driven surrogate model in a pre-selection phase of the optimisation. The adopted model should classify newly generated configurations as compliant or not with the design constraint. Configurations classified as unfeasible are disregarded, thus avoiding the computational burden of their complete evaluation. Interesting results have been obtained, both in terms of avoided configuration evaluations and the quality of the Pareto front found by the optimisation procedure.

The design of renewable-based and collective energy systems requires consumption data with fine temporal and spatial resolution. Despite the increasing deployment of smart meters, obtaining such data directly from measurements can still be challenging, particularly when attempting to gather historical data over a reasonable period for many end users. As a result, there is a need for models to simulate or predict these consumption data (e.g., hourly load profiles). Typically, these models rely on numerous specific and detailed observations, such as load type, household size for residential customers, or operating hours for commercial ones. However, gathering this level of detail becomes increasingly difficult as the number and diversity of end users increase. Therefore, this paper proposes a data-driven approach to predict hourly load profiles of heterogeneous end users using only their monthly time-of-use electricity bills as inputs. We create a training set using a limited number of hourly measurements from diverse categories of end users and, differently from other approaches aimed at classifying the end users, we develop a regression model to map monthly electricity bills to typical load profiles. Experimental results using one year of data from various end-user categories demonstrate an average normalized mean absolute error of approximately 26% for instantaneous consumption and less than 4% for time-of-use values. Comparative analysis with standard load profiles and a two-step data-driven approach based on classification reveals that our proposed method outperforms the others in terms of prediction accuracy and statistical metrics.INDEX TERMS Energy consumption, electricity demand, load modeling, data-driven modeling, nearest neighbor methods.