Accurate occupancy prediction in smart buildings is a key element to reduce building energy consumption and control HVAC systems (Heating -Ventilation and-Air Conditioning) efficiently, resulting in an increment of human comfort. This work focuses on the problem of occupancy prediction modelling (occupied / unoccupied) in smart buildings using environmental sensor data. A novel transfer learning approach was used to enhance occupancy prediction accuracy when the amounts of historical training data are limited. The proposed approach and models are applied to a case study of three office rooms in an educational building. The data sets used in this work are actual data collected from the Urban Sciences Building (USB) in Newcastle University. The results of the proposed transfer learning approach have been compared with the models from Support Vector Machine and Random Forest algorithms. The final results demonstrate that the most accurate model in this study to predict occupancy status was produced by stacked Long-Short-Term-Memory with a transfer learning framework.
Without a doubt, many remote areas have a hidden potential of energy, which can be considered for electricity production. Indeed, energy supply for remote areas is one of the most critical targets of SDGs BY the 2030 year. Based on this explanation, this paper presents a technoeconomic analysis of hybrid energy systems installable for two capital provinces of Iran, concerning SDGs targets. Firstly, a comprehensive investigation of SDGs and UN-Habitat III targets are described and then, concerning these targets and existing data gathered by the meteorological organization of Iran, a techno-economic analysis is conducted using Homer software. Regarding the high potential of renewable energies in Zahedan and Zanjan cities of Iran, implementing hybrid energy systems could be feasible for producing electrical energy as a correct policy and a good vision by policymakers and energy experts in the future. In this respect, a PV-Wind-Generator system is investigated in this paper for producing electricity in the two mentioned cities. Technical analysis of the solar energy for Zahedan is showing that the total amount of electricity production by the hybrid system is about 40,617 kWh/yr. In addition, the total amount of electricity production by this hybrid system for Zanjan is to equal 41,728 kWh/yr. Therefore, regarding this high potential of energy in these areas, investment on the solar energy for both cities has economic justification, while from the wind energy potential viewpoint, only Zahedan is proper for investment.
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