Rooftop solar photovoltaics involve laying photovoltaic solar panels on rooftops without utilizing additional land resources. This not only enhances land utilization but also effectively supports urban electricity consumption. Therefore, the scale of rooftop solar photovoltaic installations in cities is closely related to the built-up area of
Customer ServiceIn this paper we present a novel agent-based model- ing methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to...
Customer ServiceKeywords: rooftop solar photovoltaic; battery-storage systems; electric cars; discrete choice modeling; agent-based modeling 1. Introduction According to a recent estimate [1], in Italy, solar photovoltaic (PV) systems installed in buildings could reach a total nominal power of 46 GW with a yield of 50.4 TWh/year
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Customer ServiceWe apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions.
Customer ServiceIn this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy market. We first applied several linear regression models to...
Customer ServiceAbstract: The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental, and economic aspects to evaluate
Customer ServiceAgent-based modeling (ABM) is extensively used to understand and predict the adoption of
Customer ServiceGeographic information systems (GISs)-based estimation is justified as a promising approach for estimating rooftop solar photovoltaic potential, in particular, the possibility of combining GISs with LiDAR (Lighting-Detection-And-Ranging) to build robust approaches leading to accurate estimates of the rooftop solar photovoltaic potential. Accordingly, this study
Customer ServiceIn this context, this study aims to develop a hybrid model integrating an agent
Customer ServiceRooftop solar, both in the residential and the non-residential sector, is emerging rapidly as a popular source of clean electricity. Together with utility-scale photovoltaics, its future growth is essential to achieve decarbonization targets. Therefore, understanding adoption determinants for firms and households is key to efficiently promoting
Customer ServiceRooftop solar photovoltaics (RSPV) are critical for megacities to achieve low
Customer ServiceThe use of solar photovoltaic (PV) has strongly increased in the last decade. The capacity increased from 6.6 GW to over 500 GW in the 2006–2018 period [1] terestingly, the main driver for this development were investments done by home owners in rooftop PV, not investments in utility-scale PV [2], [3] fact, rooftop PV accounts for the majority of installed
Customer ServiceIn this context, this study aims to develop a hybrid model integrating an agent-based modeling (ABM) with the geographic information system and logistic regression for simulating rooftop solar photovoltaic (PV) adoption in the study area.
Customer ServiceThe rapid development of science and technology has provided abundant technical means for the application of integrated technology for photovoltaic (PV) power generation and the associated architectural design, thereby facilitating the production of PV energy (Ghaleb et al. 2022; Wu et al., 2022).With the increasing application of solar
Customer ServiceRooftop solar, both in the residential and the non-residential sector, is
Customer ServiceAs shown in Fig. 1, the rooftop solar PV adoption model based on an ABM consists of two primary components (i.e., (a) Fields/Properties; and (b) Actions in Fig. 1) with two hierarchical levels (i.e., (c) Model level; and (d) Agent level in Fig. 1). In the rooftop solar PV adoption model, each building in a regional boundary was defined as an
Customer ServiceOpportunity of rooftop solar photovoltaic as a cost-effective and environment-friendly power source in megacities Author links open overlay panel Mai Shi 1 2 3, Xi Lu 1 2 3 7, Haiyang Jiang 4, Qing Mu 1 2 3, Shi Chen 1 2 3, Rachael Marie Fleming 1, Ning Zhang 4, Ye Wu 1, Aoife M. Foley 5 6
Customer ServiceIn this paper we present a novel agent-based modeling methodology to predict rooftop solar adoptions in the residential energy
Customer Service4.1 Data. In order to construct the DDABM for rooftop solar adoption, we made use of three data sets provided by the Center for Sustainable Energy: individual-level adoption characteristics of residential solar projects
Customer ServiceRooftop solar photovoltaics (RSPV) are critical for megacities to achieve low-carbon emissions. However, a knowledge gap exists in a supply-demand-coupled analysis that considered simultaneously RSPV spatiotemporal patterns and city-accommodation capacities, a pivotal way to address solar PV intermittency issues. Here, we developed an
Customer ServiceDeveloping rooftop photovoltaics has become an important pathway towards carbon neutrality globally, but how to rationally implement rooftop photovoltaic development has not been investigated. This study presents a technical framework for optimizing the development scale and spatial layout of rooftop solar installations based on high-resolution generation
Customer ServiceUrban building rooftops provide promising locations for solar photovoltaic installations [4] and can contribute effectively to make nearly net-zero energy buildings [3]. Rooftop solar photovoltaics can be considered an effective solution for urban energy management to solve urban energy requirements and environmental problems [1].
Customer ServiceAgent-based modeling (ABM) is extensively used to understand and predict the adoption of rooftop solar photovoltaic (PV) systems by incorporating various social factors and interactions among potential adopters. For instance, the PVact model analyzes the influence of monetary evaluation and social pressure on adoption behavior, revealing that
Customer ServiceDiffusion of microgeneration technologies, particularly rooftop photovoltaic (PV), represents a key option in reducing emissions in the residential sector. We use a uniquely rich dataset from the
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