https://projectswiki.eleceng.adelaide.edu.au/projects/api.php?action=feedcontributions&feedformat=atom&user=A1714513 2026-06-17T23:05:41Z User contributions MediaWiki 1.31.4 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s2-7521_Probabilistic_forecasting_of_energy_prices_in_the_National_Electricity_Market_(NEM)&diff=15175 2020-09-20T13:37:21Z

<p>A1714513: /* Objectives */</p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> [[Category:2020s2|106]]<br /> == Introduction ==<br /> The landscape of the power system operation drastically changed since the restructuring of the industry and national electricity market establishment in 1998. These changes have been exacerbated in recent years by introducing intermittent renewable resources (such as wind and PV farms) into the grid. As a result, price volatility is more than ever. In this situation,planning to enter/leave the market and how to bid in the market became a difficult task that needs advanced decision-making tools to predict not only the most probable scenarios but also the risks involved.<br /> <br /> In this project, we want to develop a probabilistic forecasting algorithm for energy prices in the NEM considering some external factors/predictors. <br /> <br /> This project provides an opportunity to learn about electricity market and NEM operation, times series analysis and probabilistic prediction algorithms, data science and programming in Python/MATLAB, and collaborate/write a research paper/report on the study.<br /> === Project team ===<br /> ==== Project students ====<br /> * Hao Wang<br /> * Yueran Zhu<br /> ==== Supervisors ====<br /> * Dr Ali Pourmousavi Kani<br /> ==== Advisors ====<br /> <br /> === Objectives ===<br /> In this project, we want to develop a probabilistic forecasting algorithm for energy prices in the NEM considering some external factors/predictors.<br /> <br /> == Background ==<br /> === Topic 1 ===<br /> <br /> == Method ==<br /> <br /> == Results ==<br /> <br /> == Conclusion ==<br /> <br /> == References ==<br /> [1] a, b, c, &quot;Simple page&quot;, In Proceedings of the Conference of Simpleness, 2010.<br /> <br /> [2] ...</div>

A1714513 https://projectswiki.eleceng.adelaide.edu.au/projects/index.php?title=Projects:2020s2-7521_Probabilistic_forecasting_of_energy_prices_in_the_National_Electricity_Market_(NEM)&diff=15174 2020-09-20T13:34:50Z

<p>A1714513: Created page with &quot;Category:Projects Category:Final Year Projects 106 == Introduction == The landscape of the power system operation drastically changed since the res...&quot;</p> <hr /> <div>[[Category:Projects]]<br /> [[Category:Final Year Projects]]<br /> [[Category:2020s2|106]]<br /> == Introduction ==<br /> The landscape of the power system operation drastically changed since the restructuring of the industry and national electricity market establishment in 1998. These changes have been exacerbated in recent years by introducing intermittent renewable resources (such as wind and PV farms) into the grid. As a result, price volatility is more than ever. In this situation,planning to enter/leave the market and how to bid in the market became a difficult task that needs advanced decision-making tools to predict not only the most probable scenarios but also the risks involved.<br /> <br /> In this project, we want to develop a probabilistic forecasting algorithm for energy prices in the NEM considering some external factors/predictors. <br /> <br /> This project provides an opportunity to learn about electricity market and NEM operation, times series analysis and probabilistic prediction algorithms, data science and programming in Python/MATLAB, and collaborate/write a research paper/report on the study.<br /> === Project team ===<br /> ==== Project students ====<br /> * Hao Wang<br /> * Yueran Zhu<br /> ==== Supervisors ====<br /> * Dr Ali Pourmousavi Kani<br /> ==== Advisors ====<br /> <br /> === Objectives ===<br /> <br /> <br /> == Background ==<br /> === Topic 1 ===<br /> <br /> == Method ==<br /> <br /> == Results ==<br /> <br /> == Conclusion ==<br /> <br /> == References ==<br /> [1] a, b, c, &quot;Simple page&quot;, In Proceedings of the Conference of Simpleness, 2010.<br /> <br /> [2] ...</div>

A1714513