Energies

Research

17 pages, 1139 KiB

Open AccessEditor’s ChoiceArticle

Constructing Interval Forecasts for Solar and Wind Energy Using Quantile Regression, ARCH and Exponential Smoothing Methods

by John Boland

Energies 2024, 17(13), 3240; https://doi.org/10.3390/en17133240 - 1 Jul 2024

Cited by 1 | Viewed by 836

Abstract

The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the [...] Read more.

The research reported in this article focuses on a comparison of two different approaches to setting error bounds, or prediction intervals, on short-term forecasting of solar irradiation as well as solar and wind farm output. Short term in this instance relates to the time scales applicable in the Australian National Electricity Market (NEM), which operates on a five-minute basis throughout the year. The Australian Energy Market Operator (AEMO) has decided in recent years that, as well as point forecasts of energy, it is advantageous for planning purposes to have error bounds on those forecasts. We use quantile regression as one of the techniques to construct the bounds. This procedure is compared to a method of forecasting the conditional variance by use of either ARCH/GARCH or exponential smoothing, whichever is more appropriate for the specific application. The noise terms for these techniques must undergo a normalising transformation before their application. It seems that, for certain applications, quantile regression performs better, and the other technique for some other applications. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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25 pages, 4578 KiB

Open AccessEditor’s ChoiceArticle

A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

by Fabio Famoso, Ludovica Maria Oliveri, Sebastian Brusca and Ferdinando Chiacchio

Energies 2024, 17(7), 1627; https://doi.org/10.3390/en17071627 - 28 Mar 2024

Cited by 2 | Viewed by 939

Abstract

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network [...] Read more.

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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34 pages, 6290 KiB

Open AccessArticle

Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data

by Monica Borunda, Adrián Ramírez, Raul Garduno, Carlos García-Beltrán and Rito Mijarez

Energies 2023, 16(23), 7915; https://doi.org/10.3390/en16237915 - 4 Dec 2023

Cited by 4 | Viewed by 2019

Abstract

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on [...] Read more.

Wind power is an important energy source that can be used to supply clean energy and meet current energy needs. Despite its advantages in terms of zero emissions, its main drawback is its intermittency. Deterministic approaches to forecast wind power generation based on the annual average wind speed are usually used; however, statistical treatments are more appropriate. In this paper, an intelligent statistical methodology to forecast annual wind power is proposed. The seasonality of wind is determined via a clustering analysis of monthly wind speed probabilistic distribution functions (PDFs) throughout

n

years. Subsequently, a methodology to build the wind resource typical year (WRTY) for the

n + 1

year is introduced to characterize the resource into the so-called statistical seasons (SSs). Then, the wind energy produced at each SS is calculated using its PDFs. Finally, the forecasted annual energy for the

n + 1

year is given as the sum of the produced energies in the SSs. A wind farm in Mexico is chosen as a case study. The SSs, WRTY, and seasonal and annual generated energies are estimated and validated. Additionally, the forecasted annual wind energy for the

n + 1

year is calculated deterministically from the

n

year. The results are compared with the measured data, and the former are more accurate. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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12 pages, 1621 KiB

Open AccessEditor’s ChoiceArticle

An Ensemble Approach for Intra-Hour Forecasting of Solar Resource

by Sergiu-Mihai Hategan, Nicoleta Stefu and Marius Paulescu

Energies 2023, 16(18), 6608; https://doi.org/10.3390/en16186608 - 14 Sep 2023

Cited by 2 | Viewed by 1032

Abstract

Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky [...] Read more.

Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky imagery, is proposed. This study is conducted with high-quality data and high-resolution sky images recorded on the Solar Platform of the West University of Timisoara, Romania. Atmospheric factors that contribute to improving or reducing the quality of forecasts are discussed. Generally, the statistical models gain a small skill score across all forecast horizons (5 to 30 min). The machine-learning-based methods perform best at smaller forecast horizons (less than 15 min), while the all-sky-imagery-based model performs best at larger forecast horizons. Overall, for forecast horizons between 10 and 30 min, the weighted forecast ensemble with frozen coefficients achieves a skill score between 15 and 20%. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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20 pages, 5773 KiB

Open AccessArticle

WRF Parameterizations of Short-Term Solar Radiation Forecasts for Cold Fronts in Central and Eastern Europe

by Michał Mierzwiak, Krzysztof Kroszczyński and Andrzej Araszkiewicz

Energies 2023, 16(13), 5136; https://doi.org/10.3390/en16135136 - 3 Jul 2023

Cited by 1 | Viewed by 1231

Abstract

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by [...] Read more.

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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24 pages, 6660 KiB

Open AccessFeature PaperArticle

ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations

by Ewa Chodakowska, Joanicjusz Nazarko, Łukasz Nazarko, Hesham S. Rabayah, Raed M. Abendeh and Rami Alawneh

Energies 2023, 16(13), 5029; https://doi.org/10.3390/en16135029 - 28 Jun 2023

Cited by 26 | Viewed by 3313

Abstract

The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management [...] Read more.

The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management of energy systems, especially in the context of ambitious greenhouse gas emission goals. This paper presents a study on the application of auto-regressive integrated moving average (ARIMA) models for the seasonal forecasting of solar radiation in different climatic conditions. The performance and prediction capacity of ARIMA models are evaluated using data from Jordan and Poland. The essence of ARIMA modeling and analysis of the use of ARIMA models both as a reference model for evaluating other approaches and as a basic forecasting model for forecasting renewable energy generation are presented. The current state of renewable energy source utilization in selected countries and the adopted transition strategies to a more sustainable energy system are investigated. ARIMA models of two time series (for monthly and hourly data) are built for two locations and a forecast is developed. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can contribute to the stable long-term integration of solar energy into countries’ systems. However, it is crucial to develop location-specific models due to the variability of solar radiation characteristics. This study provides insights into the use of ARIMA models for solar radiation forecasting and highlights their potential for supporting the planning and operation of energy systems. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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28 pages, 7303 KiB

Open AccessArticle

Impact of PV/Wind Forecast Accuracy and National Transmission Grid Reinforcement on the Italian Electric System

by Marco Pierro, Fabio Romano Liolli, Damiano Gentili, Marcello Petitta, Richard Perez, David Moser and Cristina Cornaro

Energies 2022, 15(23), 9086; https://doi.org/10.3390/en15239086 - 30 Nov 2022

Cited by 2 | Viewed by 1747

Abstract

The high share of PV energy requires greater system flexibility to address the increased demand/supply imbalance induced by the inherent intermittency and variability of the solar resource. In this work, we have developed a methodology to evaluate the margins for imbalance reduction and [...] Read more.

The high share of PV energy requires greater system flexibility to address the increased demand/supply imbalance induced by the inherent intermittency and variability of the solar resource. In this work, we have developed a methodology to evaluate the margins for imbalance reduction and flexibility that can be achieved by advanced solar/wind forecasting and by strengthening the national transmission grid connecting the Italian market areas. To this end, for the forecasting of the day-ahead supply that should be provided by dispatchable generators, we developed three advanced load/PV/wind forecasting methodologies based on a chain or on the optimal mix of different forecasting techniques. We showed that, compared to the baseline forecast, there is a large margin for the imbalance/flexibility reduction: 60.3% for the imbalance and 47.5% for the flexibility requirement. In contrast, the TSO forecast leaves only a small margin to reduce the imbalance of the system through more accurate forecasts, while a larger reduction can be achieved by removing the grid constrains between market zones. Furthermore, we have applied the new forecasting methodologies to estimate the amount of imbalance volumes/costs/flexibility/overgenerations that could be achieved in the future according to the Italian RES generation targets, highlighting some critical issues related to high variable renewable energy share. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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25 pages, 5200 KiB

Open AccessArticle

Photovoltaic Power Generation Forecasting for Regional Assessment Using Machine Learning

by Monica Borunda, Adrián Ramírez, Raul Garduno, Gerardo Ruíz, Sergio Hernandez and O. A. Jaramillo

Energies 2022, 15(23), 8895; https://doi.org/10.3390/en15238895 - 24 Nov 2022

Cited by 11 | Viewed by 2475

Abstract

Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. [...] Read more.

Solar energy currently plays a significant role in supplying clean and renewable electric energy worldwide. Harnessing solar energy through PV plants requires problems such as site selection to be solved, for which long-term solar resource assessment and photovoltaic energy forecasting are fundamental issues. This paper proposes a fast-track methodology to address these two critical requirements when exploring a vast area to locate, in a first approximation, potential sites to build PV plants. This methodology retrieves solar radiation and temperature data from free access databases for the arbitrary division of the region of interest into land cells. Data clustering and probability techniques were then used to obtain the mean daily solar radiation per month per cell, and cells are clustered by radiation level into regions with similar solar resources, mapped monthly. Simultaneously, temperature probabilities are determined per cell and mapped. Then, PV energy is calculated, including heat losses. Finally, PV energy forecasting is accomplished by constructing the P50 and P95 estimations of the mean yearly PV energy. A case study in Mexico fully demonstrates the methodology using hourly data from 2000 to 2020 from NSRDB. The proposed methodology is validated by comparison with actual PV plant generation throughout the country. Full article

(This article belongs to the Special Issue Volume Ⅱ: Advances in Wind and Solar Farm Forecasting)

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