Minhyun Lee

Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the countrys climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

Estimating the Loss Ratio of Solar Photovoltaic Electricity Generation through Stochastic Analysis

As climate change and environmental pollution become one of the biggest global issues today, new renewable energy, especially solar photovoltaic (PV) system, is getting great attention as a sustainable energy source. However, initial investment cost of PV system is considerable, and thus, it is crucial to predict electricity generation accurately before installation of the system. This study analyzes the loss ratio of solar photovoltaic electricity generation from the actual PV system monitoring data to predict electricity generation more accurately in advance. This study is carried out with the following five steps: (i) Data collection of actual electricity generation from PV system and the related information; (ii) Calculation of simulation-based electricity generation; (iii) Comparative analysis between actual electricity generation and simulation-based electricity generation based on the seasonality; (iv) Stochastic approach by defining probability distribution of loss ratio between actual electricity generation and simulation-based electricity generation ; and (v) Case study by conducting Monte-Carlo Simulation (MCS) based on the probability distribution function of loss ratio. The results of this study could be used (i) to estimate electricity generation from PV system more accurately before installation of the system, (ii) to establish the optimal maintenance strategy for the different application fields and the different season, and (iii) to conduct feasibility study on investment at the level of life cycle.

Framework for Calculating the Rooftop Solar Photovoltaic (PV) Footprint Considering Building Electricity Supply and Demand from the Urban Level

An energy paradigm shift from fossil fuels to renewable energy played an important role in increasing the market penetration through distributed solar generation (DSG), often with a rooftop solar photovoltaic (PV) system. For a successful implementation of DSG, it is crucial to determine how much electricity from DSG is required to offset the building electricity consumption. By examining the rooftop solar PV footprint, the total area required to meet building electricity demand with DSG, it is possible to analyze the relationship between the building electricity supply and demand strategically from the urban level. Therefore, this study aims to propose a framework for calculating the rooftop solar PV footprint by considering building electricity supply and demand from the urban level. The framework proposed in this study could be applied to accurately calculate and estimate the rooftop solar PV footprint for evaluating the building energy performance considering electricity supply and demand from the urban level.

Establishment of Optimal Control Strategy of Building-Integrated Photovoltaic Blind Slat Angle by Considering Interior Illuminance and Electricity Generation

A building-integrated photovoltaic blind (BIPB), in which blind and PV system is combined to generate energy in the building exterior and reduce the heating and cooling load in building by shading function. This study aimed to establish the optimal control strategy of BIPB slat angle by considering interior illuminance and electricity generation. First, in terms of interior illuminance considering overall light (i.e., daylight and artificial illumination) and electricity generation from BIPB, it was determined that the optimal blind slat angle is 80° at all time. Second, in terms of interior illuminance considering daylight and electricity generation from BIPB, it was determined that the optimal blind slat angle is 10° (9:00), 20° (10:00–11:00, 14:00–15:00) and 30° (12:00–13:00). Based on results of this study, effective use of BIPB can be induced by providing information for optimal blind slat angle to users that are considering BIPB implementation.