scholarly journals Enhanced Day-Ahead PV Power Forecast: Dataset Clustering for an Effective Artificial Neural Network Training

2021 ◽  
Vol 5 (1) ◽  
pp. 16
Author(s):  
Andrea Matteri ◽  
Emanuele Ogliari ◽  
Alfredo Nespoli
Keyword(s):  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Energy Sources ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Network Training ◽  
Artificial Neural ◽  
Grid Operation ◽  
Artificial Neural Network Training

The increasing integration of renewable energy sources into the existing energy supply structure is challenging due to the intermittency typical of these energy sources, which implies problems of reliability and scheduling of grid operation. Concerning solar energy, the solar forecast tool predicts the photovoltaic (PV) power production and therefore permits a more efficient grid management. In this paper, the combination of clustering techniques and ANNs (Artificial Neural Networks) for day-ahead PV power forecast is analyzed. Clustering techniques are exploited to divide a dataset into different classes of days with similar weather conditions. Then, a dedicated ANN is developed for every group. The main goal is to assess the forecast improvement determined by the combination of ANNs and dataset clustering methods. Different combinations are compared on a real case study: a PV facility in SolarTechLAB, in Politecnico di Milano.

scholarly journals A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania

2020 ◽  
Vol 12 (15) ◽  
pp. 6084
Author(s):  
Simona-Vasilica Oprea ◽  
Adela Bâra ◽  
Ștefan Preda ◽  
Osman Bulent Tor
Keyword(s):  
Fuzzy Logic ◽  
Renewable Energy ◽  
Power Systems ◽  
Electricity Generation ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Sources ◽  
Fuzzy Logic Systems

Electricity generation from renewable energy sources (RES) has a common feature, that is, it is fluctuating, available in certain amounts and only for some periods of time. Consuming this electricity when it is available should be a primary goal to enhance operation of the RES-powered generating units which are particularly operating in microgrids. Heavily influenced by weather parameters, RES-powered systems can benefit from implementation of sensors and fuzzy logic systems to dynamically adapt electric loads to the volatility of RES. This study attempts to answer the following question: How to efficiently integrate RES to power systems by means of sustainable energy solutions that involve sensors, fuzzy logic, and categorization of loads? A Smart Adaptive Switching Module (SASM) architecture, which efficiently uses electricity generation of local available RES by gradually switching electric appliances based on weather sensors, power forecast, storage system constraints and other parameters, is proposed. It is demonstrated that, without SASM, the RES generation is supposed to be curtailed in some cases, e.g., when batteries are fully charged, even though the weather conditions are favourable. In such cases, fuzzy rules of SASM securely mitigate curtailment of RES generation by supplying high power non-traditional storage appliances. A numerical case study is performed to demonstrate effectiveness of the proposed SASM architecture for a RES system located in Hulubești (Dâmbovița), Romania.

scholarly journals Microgrid analysis using HOMER: a case study

DYNA ◽  
2018 ◽  
Vol 85 (207) ◽  
pp. 129-134 ◽  
Author(s):  
David Restrepo ◽  
Bonie Restrepo ◽  
Luz Adriana Trejos-Grisales
Keyword(s):  
Renewable Energy Sources ◽  
Energy Systems ◽  
Weather Conditions ◽  
Photovoltaic System ◽  
Energy Sources ◽  
System A ◽  
Current Energy ◽  
Local Weather ◽  
Economic Studies

The integration of renewable energy sources to create microgrids is drawing growing interest to address current energy-related challenges around the globe. Nevertheless, microgrids must be analyzed using specialized tools that allow to conduct operation, technical and economic studies. In that regard, this paper presents a case study in which the software HOMER Energy Pro was implemented to design and analyze the performance of a microgrid. Such microgrid comprises a photovoltaic system, a wind system and a diesel plant. The parameters of the energy systems are based on information about local weather conditions available in databases. Finally, this analysis is performed under two conditions: stand-alone and grid-tied.

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