Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation

Palmer, Johannes; Sorda, Giovanni; Madlener, Reinhard

doi:10.1016/j.techfore.2015.06.011

Cited by 140 publications

(95 citation statements)

References 35 publications

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“…Moreover, validation is not only done at the macro-level by comparison with actual adoption traces, but also at the micro-level by means of the simulated likelihood ratio relative to a baseline model. To further justify the usefulness of ML-base approach, Zhang et al (2016) actually implement and compare their model with another agent-based model of rooftop solar adoption developed by (Palmer et al, 2015), with parameters calibrated on the same dataset following the general aggregate-level calibration approach used by them. The result is very revealing, as it strongly suggests that aggregate-level calibration is prone to overfit the model to data, an issue largely avoided by calibrating individual agent behavior.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Empirically grounded agent-based models of innovation diffusion: a critical review

2017

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Innovation diffusion has been studied extensively in a variety of disciplines, including sociology, economics, marketing, ecology, and computer science. Traditional literature on innovation diffusion has been dominated by models of aggregate behavior and trends. However, the agent-based modeling (ABM) paradigm is gaining popularity as it captures agent heterogeneity and enables fine-grained modeling of interactions mediated by social and geographic networks. While most ABM work on innovation diffusion is theoretical, empirically grounded models are increasingly important, particularly in guiding policy decisions. We present a critical review of empirically grounded agent-based models of innovation diffusion, developing a categorization of this research based on types of agent models as well as applications. By connecting the modeling methodologies in the fields of information and innovation diffusion, we suggest that the maximum likelihood estimation framework widely used in the former is a promising paradigm for calibration of agent-based models for innovation diffusion. Although many advances have been made to standardize ABM methodology, we identify four major issues in model calibration and validation, and suggest potential solutions.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Empirically grounded agent-based models of innovation diffusion: a critical review

2017

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“…There have been a number of ABMs applied to residential solar adoption (Alyousef et al 2017;Zhao et al 2011;Robinson and Rai 2015;Palmer et al 2015;Borghesi et al 2013). Studies have commonly utilized a "desire factor" threshold, or a utility threshold, to represent the point at which an individual homeowner will decide to install a solar technology.…”

Section: Life Cycle and Sustainabilitymentioning

confidence: 99%

“…The resulting model included both spatial and demographic validation (Robinson and Rai 2015). Zhao et al (2011) andPalmer et al (2015) considered additional economic and behavioral characteristics. Zhao et al (2011) developed an ABM to compare solar adoption in Figure 1.…”

Section: Life Cycle and Sustainabilitymentioning

confidence: 99%

“…Overall, they found that smaller cities are more likely to incentivize solar PV adoption. Palmer et al (2015) simulated residential solar adoption in Italy by gathering data on consumer household income, environmental attitude, payback period, and social influence. Based on the results, income influenced solar PV adoption more than other utility characteristics.…”

Section: Life Cycle and Sustainabilitymentioning

confidence: 99%

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Learned Discourses: Timely Scientific Opinions

Ankley¹

2018

Integr Envir Assess & Manag

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“…In an international context, studies found that consumers' decisions to adopt renewable energy technologies were influenced by motivational, contextual, and habitual factors [7]. Some studies emphasized that residents adopted renewable energy technologies because of prospects for economic benefits or social pressure from their peers and neighbors [7][8][9]. Others suggested that environmental motivations induced people to adopt renewable energy [9].…”

Section: Introductionmentioning

confidence: 99%

The Role of Household Consumers in Adopting Renewable Energy Technologies in Kenya

Kiprop

Matsui

Maundu³

2019

Environments

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In transition to a low-carbon economy, the adoption of renewable energy (RE) technologies by energy investors, power utilities and energy consumers is critical. In developing countries like Kenya with a high rate of urbanization, this transition requires urban and rural residents' proactive responses to using renewable energy sources. In this regard, a better understanding of residents' perceptions about renewable energy investment, RE sources availability, climate change, environmental conservation and other factors can lead to more efficient and sustainable implementation of renewable energy policies. This study investigates the role Kenya's household energy consumers in urban and rural areas can play in adopting renewable energy technologies. To achieve this, a questionnaire survey was administered among 250 household consumers in Nairobi County, Makueni County, and Uasin Gishu County. Our survey analysis shows that about 84% of the respondents were interested in adopting renewable energy for their entire energy consumption mostly because of solving frequent power outages and high energy cost from the grid system. This perception did not have any correlations with income levels or any other socio-economic factors we identified. Furthermore, about 72% of the respondents showed their interests in producing and selling renewable energy to the national or local grids if government subsidies were readily available. Rural residents showed strong interests in adopting renewable energy technologies, especially solar PV solutions. However, the main impediment to their investment in renewable energy was the high cost of equipment (49%) and the intermittent nature of renewable energy (27%) resources.

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Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation

Cited by 140 publications

References 35 publications

Empirically grounded agent-based models of innovation diffusion: a critical review

Empirically grounded agent-based models of innovation diffusion: a critical review

Learned Discourses: Timely Scientific Opinions

The Role of Household Consumers in Adopting Renewable Energy Technologies in Kenya

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