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This paper presents the use of an artificial neural network for classification on a residence house that uses local air temperature and solar insulation predictions to identify patterns at the desired location, in order to obtain a stochastic distribution of the daily solar profile. This is a first step on the further creation of a short-term operation model that allows determining the technical and economic impact of stationary/mobile batteries of electric vehicles in presence of microrenewables. This shortterm operation model will be in the day-ahead perfect market operation (unit commitment) where specific changes are made to consider stationary and mobile operation.

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Short AbstractA major hurdle in the adoption and optimization of electrical vehicles is the transfer of knowledge from the dealer to the consumer. The first point of contact for most electric vehicle consumers is the dealer sales staff.Often dealerships have high turnover in sales staff and are driven to annual, quarterly and monthly sales. The majority of dealerships' focus is on closing new car sales, service and accessories. Leviton will share insights and best practices from the deployment of electrical vehicle supply equipment with four major automakers across nearly 3,000 dealerships in North America.

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There is a growing awareness that forecasting of solar irradiance is of special importance for forecasting the power output of photovoltaic (PV) systems and thus for optimizing their operation. This work presents the development of solar irradiance and PV power output forecasting models, based on artificial neural networks (ANNs), operating with a time horizon of 24 h in order to be integrated as part of home energy management systems (HEMS). The key characteristic of the proposed approach consists of employing statistical feature parameters to reduce the size of input data, while the results obtained indicate that it provides a reasonable balance between computational requirements and forecasting accuracy of the PV power output within the considered time frame.

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