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Non-residential buildings like healthcare ones contribute to energy consumption and cause a negative environmental impact. This is mainly due to the age of the buildings, their poor level of energy efficiency and the implementation of only a basic maintenance plan. Owing to the very limited budget available for public administrators, Energy Performance Contracting (EPC), that entails the involvement of an Energy Service Company (ESCo), can provide the entire or part of the capital needed for investments aimed at progressively increasing energy efficiency over their service life. In this paper, three acute hospitals and two community clinics built in Italy are analysed, in order to assess the economic feasibility of several energy renovation actions that can be included within EPC contracts. To this purpose, the outcomes of energy audits carried out in 2014 about these buildings are reported, which involved analyses of consumption measured over the previous three years and assessment of use profiles for the development of models to break down the overall consumption and to estimate potential savings. Diverse improvement strategies were assessed, consisting in either isolated or combined renovation actions and tested on the five buildings, whose comparison was carried out in terms of energy saving, pollution reduction and economic feasibility. As a result, the best combined renovation actions per each hospital were recommended, that included – but were not limited to – the improvement of the control and regulation of existing sub-systems without replacement, partial replacement of other sub-systems and integration of renewables. Finally, each scenario was evaluated through the payback period and other economic performance indexes in order to assess the feasibility of the EPC framework when applied to this case study. [ABSTRACT FROM AUTHOR]

This paper provides a comparative study of state-of-the-art means of predicting occupancy for smart heating control applications. We focus on approaches that predict the occupancy state of a home using occupancy schedules – that is, past records of the occupancy state. We ran our analysis on actual occupancy schedules covering several months for 45 homes. Our results show that state-of-the-art, schedule-based occupancy prediction algorithms achieve an overall prediction accuracy of over 80%. We also show that the performance of these algorithms is close to the theoretical upper bound expressed by the predictability of the input schedules. Building upon these results, we used ISO 13790-standard modelling techniques to analyse the energy savings that can be achieved by smart heating controllers that use occupancy predictors. Furthermore, we investigated the trade-off between achievable savings (typically 6–17% on average) and the risk of comfort loss for household residents. [ABSTRACT FROM AUTHOR]