scholarly journals Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3992
Author(s):  
Mohamed Massaoudi ◽  
Ines Chihi ◽  
Lilia Sidhom ◽  
Mohamed Trabelsi ◽  
Shady S. Refaat ◽  
...  
Keyword(s):  
Random Forest ◽  
Smart Grids ◽  
Weather Conditions ◽  
Feature Weighting ◽  
Attribute Selection ◽  
Data Driven ◽  
Short Term ◽  
Pv System ◽  
Comparative Performance ◽  
Power Forecasting

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.

scholarly journals Forecast Quality of Physics-Based and Data-Driven PV Performance Models for a Small-Scale PV System

2021 ◽  
Vol 9 ◽  
Author(s):  
Moritz Stüber ◽  
Felix Scherhag ◽  
Matthieu Deru ◽  
Alassane Ndiaye ◽  
Muhammad Moiz Sakha ◽  
...  
Keyword(s):  
Smart Grids ◽  
Time Series Data ◽  
Short Term Memory ◽  
Data Driven ◽  
Series Data ◽  
Small Scale ◽  
Pv System ◽  
Advantages And Disadvantages ◽  
Forecast Quality

In the context of smart grids, the need for forecasts of the power output of small-scale photovoltaic (PV) arrays increases as control processes such as the management of flexibilities in the distribution grid gain importance. However, there is often only very little knowledge about the PV systems installed: even fundamental system parameters such as panel orientation, the number of panels and their type, or time series data of past PV system performance are usually unknown to the grid operator. In the past, only forecasting models that attempted to account for cause-and-effect chains existed; nowadays, also data-driven methods that attempt to recognize patterns in past behavior are available. Choosing between physics-based or data-driven forecast methods requires knowledge about the typical forecast quality as well as the requirements that each approach entails. In this contribution, the achieved forecast quality for a typical scenario (day-ahead, based on numerical weather predictions [NWP]) is evaluated for one physics-based as well as five different data-driven forecast methods for a year at the same site in south-western Germany. Namely, feed-forward neural networks (FFNN), long short-term memory (LSTM) networks, random forest, bagging and boosting are investigated. Additionally, the forecast quality of the weather forecast is analyzed for key quantities. All evaluated PV forecast methods showed comparable performance; based on concise descriptions of the forecast approaches, advantages and disadvantages of each are discussed. The approaches are viable even though the forecasts regularly differ significantly from the observed behavior; the residual analysis performed offers a qualitative insight into the achievable forecast quality in a typical real-world scenario.

scholarly journals The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Abdelhakim El hendouzi ◽  
Abdennaser Bourouhou ◽  
Omar Ansari
Keyword(s):  
Absolute Error ◽  
Skill Score ◽  
Weather Data ◽  
Short Term ◽  
Pv System ◽  
Pv Systems ◽  
Forecasting Models ◽  
Power Stations ◽  
Photovoltaic Power ◽  
Power Forecasting

The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.

scholarly journals A Novel Hybrid Model for Short-Term Forecasting in PV Power Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yuan-Kang Wu ◽  
Chao-Rong Chen ◽  
Hasimah Abdul Rahman
Keyword(s):  
Power Output ◽  
Solar Power ◽  
Peak Load ◽  
Weather Conditions ◽  
Green Energy ◽  
Weather Data ◽  
Stable Operation ◽  
Short Term ◽  
Pv System ◽  
Experimental Database

The increasing use of solar power as a source of electricity has led to increased interest in forecasting its power output over short-time horizons. Short-term forecasts are needed for operational planning, switching sources, programming backup, reserve usage, and peak load matching. However, the output of a photovoltaic (PV) system is influenced by irradiation, cloud cover, and other weather conditions. These factors make it difficult to conduct short-term PV output forecasting. In this paper, an experimental database of solar power output, solar irradiance, air, and module temperature data has been utilized. It includes data from the Green Energy Office Building in Malaysia, the Taichung Thermal Plant of Taipower, and National Penghu University. Based on the historical PV power and weather data provided in the experiment, all factors that influence photovoltaic-generated energy are discussed. Moreover, five types of forecasting modules were developed and utilized to predict the one-hour-ahead PV output. They include the ARIMA, SVM, ANN, ANFIS, and the combination models using GA algorithm. Forecasting results show the high precision and efficiency of this combination model. Therefore, the proposed model is suitable for ensuring the stable operation of a photovoltaic generation system.

PV System Power Forecasting Based on Neural Network with Fuzzy Processing of Weather Factors

2013 ◽  
Vol 860-863 ◽  
pp. 172-175
Author(s):  
Yong Qiang Hu ◽  
Ming Yu Wang
Keyword(s):  
Neural Network ◽  
Weather Data ◽  
Membership Functions ◽  
Short Term ◽  
Weather Factors ◽  
Pv System ◽  
Forecasting Method ◽  
Temperature Radiation ◽  
System Power ◽  
Power Forecasting

A short-term PV system power forecasting method is presented in the paper based on neural network considering fuzzy characteristics of weather factors. Weather factors that affect PV system power output mainly include temperature, radiation intensity, rain and relative humidity which are all of strong fuzziness. The paper firstly made use of membership functions to process their fuzziness. Then, the historical power data of a PV system was put into neural network together with fuzzy processed historical weather data to train the network, therefore, neural network that be able to forecast PV power was get. Finally, data of an actual PV system in Colorado was employed to methods with and without fuzzy processing of weather factors, results show that the method with fuzzy processing is more accurate than that without fuzzy processing.

scholarly journals Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

2021 ◽  
Vol 11 (14) ◽  
pp. 6420
Author(s):  
Antonio Parejo ◽  
Stefano Bracco ◽  
Enrique Personal ◽  
Diego Francisco Larios ◽  
Federico Delfino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Electric Power ◽  
Smart Grids ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Energy Management Systems ◽  
The University ◽  
Persistence Model ◽  
Power Forecasting

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

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