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Record W2902904398 · doi:10.1109/pvsc.2017.8366033

Cost Analysis and Cost Reduction Opportunities of Residential PV System in the Japan

2017· article· en· W2902904398 on OpenAlexaboutno aff
Izumi Kaizuka, Haruki Yamaya, Takashi Ohigashi, Risa Kurihara, Osamu Ikki

Bibliographic record

Venue2017 IEEE 44th Photovoltaic Specialist Conference (PVSC) · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research Council
KeywordsTariffCost reductionQuarter (Canadian coin)BusinessPhotovoltaic systemEnvironmental economicsAgricultural economicsEnvironmental scienceEngineeringEconomicsElectrical engineeringGeography

Abstract

fetched live from OpenAlex

Since the start of the Feed-in Tariff Program in July 2012, the PV market in Japan has been growing and annual installed capacity reached 10.8 GW in 2015. Newly installed PV capacity in 2016 is estimated to be 8.6 GW. While the majority segments of the new capacity are commercial, industrial and utility scale application, installed capacity of residential application remains relative low at around 0.9 GW in 2016. With the reduction of Feed-in tariff level, installation cost for residential PV system decreased from 472 Yen/W in the 2012 to 363 Yen/kW (USD 3.15/W) in the third quarter of 2016. However, cost is still higher level in comparison with Germany, Australia and other countries. It is observed that soft cost of residential PV system is one of the major factors for higher cost. In this paper, reasons behind higher cost is analyzed based on residential cost breakdown based on hearings, supply chain survey. This paper also analyze the future cost reduction opportunities by the policy change and other factors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.304
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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