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  1. Article ; Online: Editorial for Special Issue

    Sonia Leva

    Forecasting, Vol 4, Iss 18, Pp 335-

    “Feature Papers of Forecasting 2021”

    2022  Volume 337

    Abstract: The human capability to react or adapt to upcoming changes strongly relies on the ability to forecast them [.] ...

    Abstract The human capability to react or adapt to upcoming changes strongly relies on the ability to forecast them [.]
    Keywords n/a ; Science (General) ; Q1-390 ; Mathematics ; QA1-939
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Editorial for Special Issue

    Sonia Leva

    Forecasting, Vol 3, Iss 1, Pp 135-

    “Feature Papers of Forecasting”

    2021  Volume 137

    Abstract: Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications [.] ...

    Abstract Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications [.]
    Keywords n/a ; Science (General) ; Q1-390 ; Mathematics ; QA1-939
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries

    Panagiotis Eleftheriadis / Spyridon Giazitzis / Sonia Leva / Emanuele Ogliari

    Forecasting, Vol 5, Iss 32, Pp 576-

    An Overview

    2023  Volume 599

    Abstract: In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS ... ...

    Abstract In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower.
    Keywords lithium batteries ; estimation ; data-driven ; machine learning ; state of charge ; Science (General) ; Q1-390 ; Mathematics ; QA1-939
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Day Ahead Electric Load Forecast

    Michael Wood / Emanuele Ogliari / Alfredo Nespoli / Travis Simpkins / Sonia Leva

    Forecasting, Vol 5, Iss 16, Pp 297-

    A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies

    2023  Volume 314

    Abstract: Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and ...

    Abstract Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models’ robustness can’t be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks.
    Keywords electric load ; forecasting ; neural networks ; LSTM ; EMD ; industrial ; Science (General) ; Q1-390 ; Mathematics ; QA1-939
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Irradiance Nowcasting by Means of Deep-Learning Analysis of Infrared Images

    Alessandro Niccolai / Seyedamir Orooji / Andrea Matteri / Emanuele Ogliari / Sonia Leva

    Forecasting, Vol 4, Iss 19, Pp 338-

    2022  Volume 348

    Abstract: This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images ... ...

    Abstract This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTech LAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.
    Keywords deep learning ; infrared sky images ; irradiance nowcasting ; PV production forecasting ; Science (General) ; Q1-390 ; Mathematics ; QA1-939
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Computational Intelligence in Photovoltaic Systems

    Sonia Leva / Emanuele Ogliari

    Applied Sciences, Vol 9, Iss 9, p

    2019  Volume 1826

    Abstract: Photovoltaics, among renewable energy sources (RES), has become more popular [.] ...

    Abstract Photovoltaics, among renewable energy sources (RES), has become more popular [.]
    Keywords computational intelligence ; day-ahead forecast ; photovoltaics ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Language English
    Publishing date 2019-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid

    Simone Polimeni / Alfredo Nespoli / Sonia Leva / Gianluca Valenti / Giampaolo Manzolini

    Processes, Vol 9, Iss 2, p

    Modeling and Experimental Results

    2021  Volume 323

    Abstract: Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient ... ...

    Abstract Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.
    Keywords photovoltaic (PV) forecast ; microgrid ; energy management system ; photovoltaic energy ; mixed-integer linear programming ; Chemical technology ; TP1-1185 ; Chemistry ; QD1-999
    Subject code 690
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Advanced Methods for Photovoltaic Output Power Forecasting

    Adel Mellit / Alessandro Massi Pavan / Emanuele Ogliari / Sonia Leva / Vanni Lughi

    Applied Sciences, Vol 10, Iss 2, p

    A Review

    2020  Volume 487

    Abstract: Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output ... ...

    Abstract Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic.
    Keywords photovoltaic plant ; power forecasting ; artificial intelligence techniques ; machine learning ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 690
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant

    Francesco Grimaccia / Sonia Leva / Marco Mussetta / Emanuele Ogliari

    Applied Sciences, Vol 7, Iss 6, p

    2017  Volume 622

    Abstract: Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than ... ...

    Abstract Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance.
    Keywords artificial neural network ; day-ahead forecast ; ensemble methods ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 690
    Language English
    Publishing date 2017-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning

    Alberto Dolara / Francesco Grimaccia / Sonia Leva / Marco Mussetta / Emanuele Ogliari

    Applied Sciences, Vol 8, Iss 2, p

    2018  Volume 228

    Abstract: The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) ... ...

    Abstract The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network’s main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility.
    Keywords photovoltaics ; power forecasting ; artificial neural networks ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 006
    Language English
    Publishing date 2018-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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