Fonseca Jr. João G. S.

Research field (6)： Energy chemistry , Control and systems engineering , Control and systems engineering , Mechanics and mechatronics , Robotics and intelligent systems , Thermal engineering

Research keywords (10)：日射量予測 , 太陽光発電システム , Artificial Intelligence Techniques , Exergy Analysis , Thermodynamics , エネルギーシステムシミュレーション , エゼルギー分析 , 人工の神経回路網 , 太陽光発電 , 日射量予測

Research theme for competitive and other funds (2)：

2021 - 2024 太陽光発電システム上の積雪動態の解明と予測への展開
日射量予測と太陽光発電の予測

Papers (50)：

J. Gari, da, Silva, Fonseca Jr, Y. Udagawa, T. Oozeki, K. Ogimoto. Characteristics of Day-Ahead Residual Demand, PV Power and Demand Forecasts in a Scenario of Large Penetration of PV. Proceeding of 37th European Photovoltaic Solar Energy Conference and Exhibition. 2020. 1688-1694
Joao Gari da Silva Fonseca Junior. スマートグリッドへの利用に向けた最新の日射量・太陽光発電電力量の予測技術 (第3回). スマートグリッド. 2020
Joao Gari da Silva Fonseca, Fumichika Uno, Hideaki Ohtake, Takashi Oozeki, Kazuhiko Ogimoto. Enhancements in Day-Ahead Forecasts of Solar Irradiation with Machine Learning: A Novel Analysis with the Japanese Mesoscale Model. Journal of Applied Meteorology and Climatology. 2020. 59. 5. 1011-1028
Joao Gari da Silva Fonseca Junior. スマートグリッドへの利用に向けた最新の日射量・太陽光発電電力量の予測技術(第2回). スマートグリッド. 2020
Rajitha Udawalpola, Taisuke Masuta, Hideaki Ohtake, Joao Gari, da Silva, Fonseca Junior. Optimal Scheduling of Generators and BESS using Forecasting in Power System with Extremely large Photovoltaic Generation. IEEE PES PowerTech Milano 2019 (POWERTECH2019). 2019

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MISC (49)：

海崎光宏, 宇野史睦, 大関崇, 西辻裕紀, 西辻裕紀, 宇田川佑介, 宇田川佑介, DA SILVA, FONSECA JUNIOR, Joao Gari, et al. 太陽光発電の発電予測・把握技術におけるアップスケーリングの誤差解析. 電気学会電力・エネルギー部門大会論文集(CD-ROM). 2018. 2018. ROMBUNNO.111
大関崇, 海崎光宏, 宇野史睦, 西辻裕紀, 西辻裕紀, 宇田川佑介, 宇田川佑介, DA SILVA, FONSECA JUNIOR, Joao Gari, et al. 太陽光発電の発電予測・把握技術における利用するデータ数による分析結果の影響評価. 電気学会電力・エネルギー部門大会論文集(CD-ROM). 2018. 2018. ROMBUNNO.110
宇田川佑介, 宇田川佑介, 西辻裕紀, 西辻裕紀, 荻本和彦, DA SILVA, FONSECA JUNIOR, Joao Gari, 海崎光宏, 大関崇, et al. 太陽光発電出力短時間予測を考慮した高頻度発電機起動停止計画. 電気学会電力・エネルギー部門大会論文集(CD-ROM). 2018. 2018. ROMBUNNO.262
Improvement and Effect of Unit Commitment Using PV Yield Forecast on the Morning of Operation Day. 2017. 2017. 89. 133-138
Evaluation of Supply-Demand Operation Using Photovoltaic Generation Forecast in Power System with Extremely Large Integration of Photovoltaic Generation. 2016. 2016. 67. 39-42

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Books (4)：

Machine Learning Applications on Integration of Renewable Energy to Power Systems
2022
連載スマートグリッドへの利用に向けた最新の日射量・太陽光発電電力量の予測技術 (第3回)
大河出版 2020
連載:スマートグリッドへの利用に向けた最新の日射量・太陽光発電電力量の予測技術(第2回)
大河出版 2020
次世代電力システム設計論 : 再生可能エネルギーを活かす予測と制御の調和
オーム社 2019 ISBN:9784274507533

Lectures and oral presentations (40)：

A Comparison of Two Methods to Forecast Residual Demand One Day Ahead of Time
(Solar World Congress 2021 2021)
Initial Results on Forecasting Residual Power Demand with Gradient Boosting Trees
(The 2020 Annual Conference of Power & Energy Society IEE of Japan 2020)
Characteristics of Day-Ahead Residual Demand, PV Power and Demand Forecasts in a Scenario of Large Penetration of PV
(37th European Photovoltaic Solar Energy Conference and Exhibition 2020)
Characterizing Day-ahead Forecast Errors of PV Power in Hokkaido: An Assessment with 2 Years of Data
(Annual Conference of Power & Energy Society IEE of Japan 2019)
On the Improvement of Day-ahead Forecasts of Solar Irradiation with Simple Ensembles and Training Data Selection in Japan: A Countrywide Assessment
(8th International Workshop on Integration of Solar Power into Power Systems, 2018 2018)

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