Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The present contribution offers evidence regarding the possibility of obtaining reasonable photovoltaic por forecasts without using ather data and with simple data-driven models. The lack of ather data as input stems from the fact that the constant obtainment of forecast ather data might become too expensive or that communication with ather services might fail, but still accurate planning and scheduling decisions have to be conducted. Therefore, accurate one-day ahead forecasting models with only information of past generated por as input for offline photovoltaic systems or as backup in case of communication failures are of interest. The results contained in the present contribution, obtained using a freely available dataset, provide a baseline with which more complex forecasting models can be compared. Additionally, it will also be shown that the presented ather-free data-driven models provide better forecasts than a trivial persistence technique for different forecast horizons. The methodology used in the present work for the data preprocessing and the creation and validation of forecasting models has a generalization capacity and thus can be used for different types of time series as ll as different data mining techniques.
['Jorge Ángel González Ordiano', 'Simon Waczowicz', 'M. Reischl', 'Ralf Mikut', 'Veit Hagenmeyer']
I am writing a research paper on a idea Photovoltaic power forecasting using simple data-driven models without weather data
[{"from": "human", "value": "I am writing a research paper on a idea Photovoltaic power forecasting using simple data-driven models without weather data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The present contribution offers evidence regarding the possibility of obtaining reasonable photovoltaic por forecasts without using ather data and with simple data-driven models. The lack of ather data as input stems from the fact that the constant obtainment of forecast ather data might become too expensive or that communication with ather services might fail, but still accurate planning and scheduling decisions have to be conducted. Therefore, accurate one-day ahead forecasting models with only information of past generated por as input for offline photovoltaic systems or as backup in case of communication failures are of interest. The results contained in the present contribution, obtained using a freely available dataset, provide a baseline with which more complex forecasting models can be compared. Additionally, it will also be shown that the presented ather-free data-driven models provide better forecasts than a trivial persistence technique for different forecast horizons. The methodology used in the present work for the data preprocessing and the creation and validation of forecasting models has a generalization capacity and thus can be used for different types of time series as ll as different data mining techniques."}]