The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.

With the rapid industrialization and urbanization, suburban areas have been developed to accommodate the sudden demand of the population. However, recent problems such as low fertility and aging induces urban shrinkage by reducing the urban population and the economy in old areas around the suburbs. As urban shrinkage causes inequality among residents in terms of the opportunities to access public services, the enhancement of the accessibility of public services is crucial to achieve inclusive growth. This paper proposes a framework for supplying public services based on the transit-oriented development (TOD) concept with geographic information system (GIS) analysis technique. A total of 24 indices, 4 criteria for 6 public services, are measured and weighted by the entropy method to find vulnerable residential buildings with a poor environment in Jung-nang district, Seoul. With a spatial analysis based on this weight value of residential buildings and the TOD concept, old commercial buildings are selected as candidate buildings for public services. According to the derived results, one candidate building as a public service can improve the environment of 3% to 8% of vulnerable residential buildings. The proposed decision-making methods can provide a valuable reference for selecting the location of public services by computational analysis with GIS.

A calibrated building simulation model was developed to assess the energy performance of a large historic research building. The complexity of space functions and operational conditions with limited availability of energy meters makes it hard to understand the end-used energy consumption in detail and to identify appropriate retrofitting options for reducing energy consumption and greenhouse gas (GHG) emissions. An energy simulation model was developed to study the energy usage patterns not only at a building level, but also of the internal thermal zones, and system operations. The model was validated using site measurements of energy usage and a detailed audit of the internal load conditions, system operation, and space programs to minimize the discrepancy between the documented status and actual operational conditions. Based on the results of the calibrated model and end-used energy consumption, the study proposed potential energy conservation measures (ECMs) for the building envelope, HVAC system operational methods, and system replacement. It also evaluated each ECM from the perspective of both energy and utility cost saving potentials to help retrofitting plan decision making. The study shows that the energy consumption of the building was highly dominated by the thermal requirements of laboratory spaces. Among other ECMs the demand management option of overriding the setpoint temperature is the most cost effective measure.