Analiza szeregów czasowych produkcji energii ze źródeł odnawialnych pod kątem niezależności energetycznej wybranego obszaru

Sabat, Michał; Baczyński, D.; Szafranek, Karolina

doi:10.15199/48.2017.09.02

Cited by 3 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tak nadwyżka, jak i deficyt wytwarzanej energii negatywnie wpływa na funkcjonowanie sieci elektroenergetycznej. Niezbędnym elementem poprawnego funkcjonowania systemu energetycznego jest dostosowanie wygenerowanej energii do aktualnego zapotrzebowania [1]. O ile znajomość zapotrzebowania pozwala na dostosowanie pozyskiwanej energii w elektrowniach konwencjonalnych, to nie jest to do końca możliwe w elektrowniach opartych o źródła odnawialne.…”

Section: Wstępunclassified

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

Idźkowski,

Sumorek

2024

PAR

View full text Add to dashboard Cite

The article presents data analysis for predicting energy production in photovoltaic (PV) power plant systems. The purpose of long-term forecasts is to determine the effectiveness of preventive actions and manage the power system effectively. Climate variables affecting the production of electricity in photovoltaic systems were analyzed. Forecasting methods using machine learning techniques such as Multi-Layer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were compared. In addition, metrics were selected to determine the quality of forecasts. Determining the quality of forecasts was based on the actual varying conditions, not on the weather forecast data. The way of data preparation to create forecasting models were presented and the models with the best metrics were selected. For this purpose, the Scikit-learn library was used to create scripts in Python. The best results were obtained for regression models: MLPRegressor, CatBoostRegressor and Support Vector Regression. Actual measurement data from a system of optimally-positioned panels with a power of 3.0 kWp were used. For the MLPRegressor model, the highest coefficient of determination 0.605 was achieved with the smallest root-mean-square error of 1.79 KWh.

show abstract

Section: Wstępunclassified

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

Idźkowski,

Sumorek

2024

PAR

View full text Add to dashboard Cite

show abstract

“…Accurate forecasts enable more effective control of electrical power systems [1]. Costs minimization of conventional power plants production or costs minimization of energy bought in energy cluster area, in microgrid area or by prosumer can be given as examples of optimization objectives [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of possibilities to improve quality of spatial wind speed forecasts for efficient forecasting of electric energy production in onshore wind farms in Poland

et al. 2019

View full text Add to dashboard Cite

The most important factor responsible for the quality of energy production forecasts in wind farms is the accurate wind speed forecast. An extensive statistical analysis of meteorological data (NWP) from 16 base nodes of the "300" grid in the "Łódź" area was made. The intention of the statistical analysis was to select potential explanatory variables for models predicting wind speed in the remaining 206 nodes of the grid’s mesh. Next, tests of selected prognostic methods were performed in order to compare their effectiveness with bilinear method which is not computationally complex. It should be emphasized that the main problem in spatial wind speed forecasting is the very large number of nodes for which the forecasts are calculated. As a consequence, more advanced and computationally complex forecasting methods cannot be applied in practice due to too long calculations time and difficulties in huge amounts of data processing. Conclusions with proposals of preferred forecasting methods that could be used in practice were developed.

show abstract

Historical Buildings Potential to Power Urban Electromobility: State-of-the-Art and Future Challenges for Nearly Zero Energy Buildings (nZEB) Microgrids

et al. 2022

View full text Add to dashboard Cite

The growing need for electric energy is forcing the construction industry to greater integrate energy production systems based on renewable energy sources. The energy ought to be used not only to support functions of the building but also to charge electric vehicles, whose number has been increasing for the last few years. However, implementation of RES (Renewable Energy Sources) systems in already existing buildings is problematic. Basing on an example of a historical building, the article presents the conversion of a facility into a nearly zero-energy building, where energy surplus may be used to charge EVs (Electric Vehicles). Interdisciplinary research describes energy consumption of the EV in real driving conditions, taking into consideration changing weather conditions and an option of energy being produced by buildings operating in an urban agglomeration: it stipulates the time needed to charge the vehicle, depending on the charging We removed dot, according to email in submitting system, please confirm.method, as well as an energy potential of adapting the solution in selected periods of building operation. The summary presents how electromobility can be supported by the construction industry.

show abstract

Analiza szeregów czasowych produkcji energii ze źródeł odnawialnych pod kątem niezależności energetycznej wybranego obszaru

Cited by 3 publications

References 5 publications

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

The Use of Machine Learning Algorithms to Forecast Energy Production in a Small PV System

Analysis of possibilities to improve quality of spatial wind speed forecasts for efficient forecasting of electric energy production in onshore wind farms in Poland

Historical Buildings Potential to Power Urban Electromobility: State-of-the-Art and Future Challenges for Nearly Zero Energy Buildings (nZEB) Microgrids

Contact Info

Product

Resources

About