Abstract
This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation.
Highlights
Modern regional electric power systems (EPS) are characterized by an increasing share of renewable energy sources (RES)
The task of RES power generation implementation is directly related to the task of electric energy generation forecasting, since the lack of renewable energy sources’ reliable forecasts entails the need to constantly maintain a full reserve of active power in the power system [1], which means the need for an extra regulation response from thermal generation and its operation in uneconomical modes and/or regulation of the power grid congestion, which in turn causes the problem of switched on power generation excess capacities at the regional level, and on a national scale
The problems of energy production forecasting at power generation facilities using various types of RES are associated with the problem of the stochastic nature of their operation modes
Summary
Modern regional electric power systems (EPS) are characterized by an increasing share of renewable energy sources (RES). Nowadays Russia is actively in the process of implementing new solar power plants, and the main problem is the availability of initial and retrospective data for developing a forecasting model. In this regard, there is a need to develop a specialized software package adapted to Russian realities and allowing forecasting of solar irradiation at the installation site of solar panels with subsequent day-ahead forecasting of electrical energy production. The authors provided a possible solution to the problem of solar power plants generation forecasting, based on the generalized open-source weather data, lacking the necessary features, characterizing specific meteorological events and conditions. A step-by-step procedure was introduced for better cloudy days forecasting, and the practical implementation results were discussed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
