DOCSLIB.ORG

Solar Power Forecasting Performance – Towards Industry Standards

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Solar Power Forecasting Performance – Towards Industry Standards Solar Power Forecasting Performance – Towards Industry Standards V. Kostylev and A. Pavlovski within utility and ISO operations. State-of-the-art solar Abstract-- Due to the rapid increase in deployment and high power forecasting is seen as a major tool to address the penetration of solar power generation worldwide, solar power risks related to the high share of variable solar generation generation forecasting has become critical to variable within the electricity mix, limiting curtailment of generation integration planning, and within utility and generation and reducing idle backup capacity. Moreover, it independent system operator (ISO) operations. Utilities and ISOs require day ahead and hour ahead as well as intra-hour is seen as an important component of the Smart Grid solar power forecasts for core operations - solar power toolbox enabling efficient demand-side management and producers and energy traders also require high quality solar demand response measures. power forecasts. Currently all major participants in the electricity value As a result of the erroneously perceived simplicity of solar chain: solar power producers, utilities and ISOs – show radiation forecasting, very often non-repeatable, poorly considerable interest in high-precision solar power forecasts explained or obscure estimates of solar power forecast performance are used. This creates uncertainty with the and imply their own technical requirements to forecast quality of forecasting service, as well as unrealistic outputs and quality. Utilities and ISOs require solar power expectations of possible forecast precision. As a result, there is forecasts for core operations; solar power producers, in an immediate need for defining a common methodology for certain jurisdictions, are mandated to provide power evaluating forecast performance, establishing verification forecasts by their power purchase agreements. procedures, and setting common standards for industry- approved quality of solar forecast performance. There are, however, no industry standards defining Solar power forecast quality claims can be easily verified applicable and credible data sources, forecast technologies when the source of forecast is known. Most often the offered and validating procedures established thus far. This creates power generation forecasts are based on publically available uncertainty with the quality of forecasting service, as well results of Numerical Weather Prediction (NWP) models and as unrealistic expectations of possible forecast precision. To on the use of empirical relationships between solar resource enable the industry with high-precision solar forecasts there and generated power at a specific plant. The quality of these is an immediate need for defining a common methodology forecasts is limited by the quality of the NWP models utilized, which is known. Less frequently, solar radiation is estimated for evaluating forecast performance, establishing based on proprietary models such as satellite-based or total verification procedures and setting common standards for sky imager–based cloud cover and radiation forecasts. In such industry-approved quality of solar forecast performance. cases, there are also known limits to the accuracy of At present, there are no widely recognized standards or prediction which can help objectively evaluate claims of the recommended practices and procedures. There is an forecast service companies. immediate need in developing recommended practices for This paper is proposing a set of standards for evaluating intra hour, hour ahead, day ahead and week ahead solar solar radiation forecasting techniques which would assist in power forecast performance. The proposed standards are producing more reliable forecasts, optimally utilizing the based on sound methodologies and extensive field practice and current levels of meteorological science and computer offer a solid ground for reliable inter-agency comparisons of technologies. Such recommendations depend on the degree forecast performance. of understanding of limitation of the existing approaches, particularly NWP and statistical methods commonly used Index Terms — Forecast standards, RMSE, bias, persistence, NWP, performance evaluation. by the solar forecast industry. This publication presents an approach to developing industry standards in solar power I. INTRODUCTION forecasting with the focus on evaluation of solar forecast performance. UE to the rapid increase in deployment and high D penetration of solar power in electricity grids A. Solar Power Forecast Time Scales worldwide, solar power generation forecasting has become The most critical technical requirements to solar power critical to variable generation integration planning and forecasting are defined by forecast time scales demanded by the electricity value chain participants. This group, V. Kostylev and A. Pavlovski are with Green Power Labs Inc., One however, does not provide clear and detailed explanation of Research Dr. Dartmouth, Nova Scotia, Canada (e-mail: the practical uses of these forecasts in reference to practical [email protected]). benefits. forecast evaluation is a matter of a separate study (being Most common industry-requested operational forecasts prepared for publication elsewhere) and we will offer only and their corresponding granularity are the following: several observations on metrics deserving the highest Intra–Hour: 15 minutes to 2 hours ahead with 30 attention. RMSE statistic is the most commonly reported in seconds to 5 minute granularity (relates to ramping forecast accuracy claims. While it is a good measure of events, variability related to operations) forecast uncertainty the published values are often non- Hour Ahead: One to 6 hours ahead with hourly informative because: 1) When expressed as relative RMSE granularity (related to load following forecasting) (%) details are often missing on normalization (to mean or Day Ahead: One to 3 days ahead with hourly range of observed values for the analysis period), 2) Often granularity (relates to unit commitment, transmission decrease in RMSE compared to other methods or scheduling, and day ahead markets) persistence is discussed instead of actual values, and 3) In Medium-term: Week to 2 months ahead, with daily analyses of power generation RMSE is often expressed in granularity (hedging, planning, asset optimisation) percent of rated power rather than observed power output. Long-term: typically one or more years, with diurnal MAE has a specific meaning and it is loosely related to monthly and annual granularity (long-term time series RMSE because it puts less emphasis on the extreme analysis, resource assessment, site selection, and discrepancies between forecasted and observed values. MBE bankable documentation) also has different meaning and value to forecast Achieving high-accuracy forecasts at each of these time performance evaluation. It relates more to general over, or scales imposes specific requirements to applicable solar under-prediction over the analysis time span, rather than to radiation models, data sources, and forecasting techniques predictive power of forecast. Therefore, the values of these converting available data into quality solar power forecasts. three statistics tell different stories about forecast quality Day Ahead forecasts are needed for operational planning, and their published values depend on the time span of switching sources, programming backup, and short-term validation analysis, use of sunlight hours only, and data power purchases, as well as for planning of reserve usage, aggregation techniques which could render these values and peak load matching [1]. Medium-term forecasts are incomparable between studies. concerned with planning, plant optimization, risk assessment, while Long-term forecasts (also known as B. Use of Naïve Models resource assessment) are targeting return on investment A number of naïve approaches is commonly used as estimates. Along this time continuum different forecasting benchmarks for demonstrating relative accuracy of approaches and evaluation techniques should be used. forecasts. Persistence, as a forecasting principle, can be used for producing forecasts for minutes to hours-ahead II. EVALUATION OF FORECAST QUALITY time scales, and less commonly to forecast day ahead at A. Interpretation of statistical measures clear sky conditions. At the same time, persistence–based forecasts are the most commonly used for comparison with Forecast performance testing is often approached from commercial forecast performance. Similarly to that of other established analytical practices without clear understanding performance metrics, the methodological details of of relevance of the performance measures to particular ‘persistence–based comparisons’ are commonly poorly management applications (e.g. plant operation and described. management, energy trading etc.). This leads to reporting a General logic of the persistence based testing is the variety of metrics for a subset of forecast scales of practical assumption of no change, i.e. forecasted conditions assumed importance. Mean absolute error (MAE), mean bias error similar to current. Various variations of persistence-based (MBE) and root mean square error (RMSE) are the three modeling in solar radiation forecasts are 1) assuming that most commonly used statistics to test solar radiation the forecasted values equal current values (e.g. [3]) 2) forecast performance. In evaluations of solar radiation assuming that the relative values of
Load more

Recommended publications
  • History and Trends in Solar Irradiance and PV Power Forecasting: a Preliminary Assessment and Review Using Text Mining
    History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining Dazhi Yanga,∗, Jan Kleisslb, Christian A. Gueymardc, Hugo T. C. Pedrob, Carlos F. M. Coimbrab aSingapore Institute of Manufacturing Technology, Agency for Science, Technology and Research (A∗STAR), Singapore bCenter for Renewable Resources and Integration, Center for Energy Research Department of Mechanical and Aerospace Engineering, University of California, San Diego, CA, USA cSolar Consulting Services, Colebrook, NH, USA Abstract Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and orga- nizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineer- ing topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.
    [Show full text]
  • World Bank Document
    July 2019 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized © 2019 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: [email protected]. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above or to [email protected]. Cover Image A system in University of British Columbia (UBC) with pyranometer and pyrgeometer for meteorological observations related to solar insolation.
    [Show full text]
  • Probabilistic Solar Power Forecasting: Long Short-Term Memory Network Vs Simpler Approaches
    Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches Vinayak Sharmaa,∗, Jorge Angel´ Gonz´alezOrdianob,c, Ralf Mikutb, Umit Calid aUniversity of North Carolina at Charlotte bInstitute for Automation and Applied Informatics, Karlsruhe Institute of Technology cColorado State University dNorwegian University of Science and Technology Abstract The high penetration of volatile renewable energy sources such as solar, make methods for coping with the uncertainty associated with them of paramount importance. Probabilistic forecasts are an example of these methods, as they assist energy planners in their decision-making process by providing them with information about the uncertainty of future power generation. Currently, there is a trend towards the use of deep learning probabilistic forecasting methods. However, the point at which the more complex deep learning methods should be preferred over more simple approaches is not yet clear. Therefore, the current article presents a simple comparison between a long short-term memory neural network and other more simple approaches. The comparison consists of training and comparing models able to provide one-day-ahead probabilistic forecasts for a solar power system. Moreover, the current paper makes use of an open source dataset provided during the Global Energy Forecasting Competition of 2014 (GEFCom14). Keywords: GEFCom14, Neural Networks, Quantile Regressions, LSTM, Probabilistic Forecasting 1. Introduction arXiv:2101.08236v1 [cs.LG] 20 Jan 2021 Over the past couple of years solar power has become one of the most pop- ular renewable energy sources (RES). Unfortunately, the generation of solar power depends completely on the Sun [5]. This dependency on weather adds uncertainty and variability to the generation of solar power.
    [Show full text]
  • Solar Forecasting: Maximizing Its Value for Grid Integration
    Solar Forecasting: Maximizing its value for grid integration Introduction The forecasting of power generated by variable energy resources such as wind and solar has been the focus of academic and industrial research and development for as long as significant amounts of these renewable energy resources have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective resources, with wind forecasting having achieved a more mature state-of-the-art compared to its solar equivalent [Lew 2010]. Still in the last 5 years, there has been substantial and material progress in the state-of-the-art of solar forecasting [Kleissl 2016]. Numerical Weather Prediction (NWP) models became more sophisticated in assessing cloud interactions with aerosols; infrared satellite imagery allowed discovery of pre-sunrise cloud formations; advanced data processing methods such as deep machine learning became increasingly accessible; probabilistic forecasts began replacing deterministic ones; and, in balancing areas with high PV penetration, solar forecasts are now used operationally. Bulk grid integration As solar electricity penetration in the distribution grid is increasing, the power generated by those Distributed Energy Resources (DERs) needs to be taken into account in the operations and planning of IPPs, ISOs, and Balancing Authorities [Mills 2013]. Since the reliable performance of the bulk grid depends on the balancing of a continuously varying load with equal amounts of generation, knowledge of the load ahead of time (forecasting) is necessary for the economically optimal – and technically feasible – dispatch of generation sources. Solar electricity generation presents two challenges to the process above. First, a very large fraction of it comes from distributed PV systems (DPV) that are connected behind-the-meter (BTM) and are thus only visible to the system operator as load.
    [Show full text]
  • Wind Generation Forecasting Methods and Proliferation of Artificial Neural
    sustainability Review Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend Muhammad Shahzad Nazir 1,* , Fahad Alturise 2 , Sami Alshmrany 3, Hafiz. M. J Nazir 4, Muhammad Bilal 5 , Ahmad N. Abdalla 6, P. Sanjeevikumar 7 and Ziad M. Ali 8,9 1 Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China 2 Computer Department, College of Science and Arts in Ar Rass, Qassim University, Ar Rass 51921, Saudi Arabia; [email protected] 3 Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia; [email protected] 4 Institute of Advance Space Research Technology, School of Networking, Guangzhou University, Guangzhou 510006, China; [email protected] 5 School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 6 Faculty of Information and Communication Engineering, Huaiyin Institute of Technology, Huai’an 223003, China; [email protected] 7 Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark; [email protected] 8 College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia; [email protected] 9 Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt * Correspondence: [email protected] or [email protected]; Tel.: +86-1322-271-7968 Received: 8 April 2020; Accepted: 23 April 2020; Published: 6 May 2020 Abstract: To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries.
    [Show full text]
  • Photovoltaic and Solar Forecasting: State of the Art
    Photovoltaic and Solar Forecasting: State of the Art Forecast PV power Actual PV power Report IEA PVPS T14‐01:2013 Photo credits cover page Upper left image: Environment Canada, Data courtesy of NOAA (February 27, 2013) Upper right image: Dave Turcotte, CanmetENERGY, Natural Resources Canada INTERNATIONAL ENERGY AGENCY PHOTOVOLTAIC POWER SYSTEMS PROGRAMME Photovoltaic and Solar Forecasting: State of the Art IEA PVPS Task 14, Subtask 3.1 Report IEA‐PVPS T14‐01: 2013 October 2013 ISBN 978‐3‐906042‐13‐8 Authors: Sophie Pelland, [email protected] Jan Remund, [email protected] Jan Kleissl, [email protected] Takashi Oozeki, [email protected] Karel De Brabandere, [email protected] Contents Contents ................................................................................................................................................... 1 Foreword .................................................................................................................................................. 2 Acknowledgements .................................................................................................................................. 3 Executive summary .................................................................................................................................. 3 1. Introduction ..................................................................................................................................... 4 2. Photovoltaic forecasting and link to solar forecasting ...................................................................
    [Show full text]
  • Solar Photovoltaic Forecasting of Power Output Using LSTM Networks
    atmosphere Article Solar Photovoltaic Forecasting of Power Output Using LSTM Networks Maria Konstantinou, Stefani Peratikou and Alexandros G. Charalambides * Department of Chemical Engineering, Cyprus University of Technology, Corner of Athinon and Anexartisias, 57, Lemesos 3603, Cyprus; [email protected] (M.K.); [email protected] (S.P.) * Correspondence: [email protected]; Tel.: +357-25-002306; Fax: +357-25-002668 Abstract: The penetration of renewable energies has increased during the last decades since it has become an effective solution to the world’s energy challenges. Among all renewable energy sources, photovoltaic (PV) technology is the most immediate way to convert solar radiation into electricity. Nevertheless, PV power output is affected by several factors, such as location, clouds, etc. As PV plants proliferate and represent significant contributors to grid electricity production, it becomes increasingly important to manage their inherent alterability. Therefore, solar PV forecasting is a pivotal factor to support reliable and cost-effective grid operation and control. In this paper, a stacked long short-term memory network, which is a significant component of the deep recurrent neural network, is considered for the prediction of PV power output for 1.5 h ahead. Historical data of PV power output from a PV plant in Nicosia, Cyprus, were used as input to the forecasting model. Once the model was defined and trained, the model performance was assessed qualitative (by graphical tools) and quantitative (by calculating the Root Mean Square Error (RMSE) and by applying the k-fold cross-validation method). The results showed that our model can predict well, since the RMSE gives a value of 0.11368, whereas when applying the k-fold cross-validation, the mean of the resulting RMSE values is 0.09394 with a standard deviation 0.01616.
    [Show full text]
  • Arxiv:2010.04715V2 [Cs.LG] 14 Oct 2020
    Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models Eric Zelikman∗1, Sharon Zhou∗1, Jeremy Irvin∗1 Cooper Raterink1, Hao Sheng1, Anand Avati1 Jack Kelly2, Ram Rajagopal1, Andrew Y. Ngy 1, David Gagney 3 1Stanford University, 2Open Climate Fix, 3National Center for Atmospheric Research {ezelikman,sharonz,jirvin16,crat,avati}@cs.stanford.edu,[email protected] [email protected], [email protected], [email protected], [email protected] Abstract Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting. 600 True Predicted 500 400 300 200 100 Global Horizontal Irradiance (GHI) 0 Time (≈ 8 days in January 2018) 0 200 400 600 800 1000 Figure 1: Several days of 30-min horizon NGBoost irradiance forecasts on the Sioux Falls station [1]. 1 Introduction arXiv:2010.04715v2 [cs.LG] 14 Oct 2020 Increasing adoption of renewable energy in the electricity sector is essential to the reduction of anthropogenic greenhouse gas emissions, but significant production increases must occur in order to phase out fossil fuel use [2, 3].
    [Show full text]
  • Day-Ahead Forecasting of Solar Power Output from Photovoltaic Plants in the American Southwest
    Renewable Energy 91 (2016) 11e20 Contents lists available at ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest * David P. Larson, Lukas Nonnenmacher, Carlos F.M. Coimbra Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center of Excellence in Renewable Energy Integration and Center for Energy Research, University of California San Diego, La Jolla, CA 92093, USA article info abstract Article history: A forecasting method for hourly-averaged, day-ahead power output (PO) from photovoltaic (PV) power Received 2 July 2015 plants based on least-squares optimization of Numerical Weather Prediction (NWP) is presented. Three Received in revised form variations of the forecasting method are evaluated against PO data from two non-tracking, 1 MWp PV 30 November 2015 plants in California for 2011e2014. The method performance, including the inter-annual performance Accepted 8 January 2016 variability and the spatial smoothing of pairing the two plants, is evaluated in terms of standard error Available online xxx metrics, as well as in terms of the occurrence of severe forecasting error events. Results validate the performance of the proposed methodology as compared with previous studies. We also show that the Keywords: Forecasting bias errors in the irradiance inputs only have a limited impact on the PO forecast performance, since the Solar power output method corrects for systematic errors in the irradiance forecast. The relative root mean square error Photovoltaics (RMSE) for PO is in the range of 10.3%e14.0% of the nameplate capacity, and the forecast skill ranges from Numerical weather prediction 13% to 23% over a persistence model.
    [Show full text]
  • SMART GRIDS and RENEWABLES: a Guide for Effective Deployment TABLE of CONTENTS
    IRENA International Renewable Energy Agency SMART GRIDS AND RENEWABLES A Guide for Effective Deployment Working Paper Working November 2013 Copyright © IRENA 2013 Unless otherwise indicated, material in this publication may be used freely, shared or reprinted, so long as IRENA is acknowledged as the source. About IRENA The International Renewable Energy Agency (IRENA) is an intergovernmental organisation that supports countries in their transition to a sustainable energy future, and serves as the principal platform for international cooperation, a centre of excel- lence, and a repository of policy, technology, resource and financial knowledge on renewable energy. IRENA promotes the widespread adoption and sustainable use of all forms of renewable energy, including bioenergy, geothermal, hydropower, ocean, solar and wind energy in the pursuit of sustainable development, energy access, energy security and low-carbon eco- nomic growth and prosperity. www.irena.org Acknowledgements The work for the preparation of this paper was led by Paul Komor and Anderson Hoke, University of Colorado, Boulder, Colo- rado, USA. The paper benefitted from very valuable comments from Rainer Bacher (Bacher Energie, Switzerland), Melissa Chan (Navigant Consulting, USA), Kazuyuki Takada (New Energy and Industrial Technology Development Organization, Japan), Matt Wakefield (EPRI, USA). Authors: Ruud Kempener (IRENA), Paul Komor and Anderson Hoke (University of Colorado) For further information or to provide feedback, please contact: Ruud Kempener, IRENA Innovation and Technology Centre. E-mail: [email protected] or [email protected]. Disclaimer The designations employed and the presentation of materials herein do not imply the e xpression of any opinion whatsoever on the part of the International Renewable Energy Agency concerning the legal status of any country, territory, city or area, or concerning their authorities or the delimitation of their frontiers or boundaries.
    [Show full text]
  • A Suite of Metrics for Assessing the Performance of Solar Power Forecasting
    Available online at www.sciencedirect.com ScienceDirect Solar Energy 111 (2015) 157–175 www.elsevier.com/locate/solener A suite of metrics for assessing the performance of solar power forecasting Jie Zhang a, Anthony Florita a, Bri-Mathias Hodge a,⇑, Siyuan Lu b, Hendrik F. Hamann b, Venkat Banunarayanan c, Anna M. Brockway c a National Renewable Energy Laboratory, Golden, CO 80401, USA b IBM TJ Watson Research Center, Yorktown Heights, NY 10598, USA c U.S. Department of Energy, Washington, D.C. 20585, USA Received 8 August 2014; received in revised form 2 October 2014; accepted 12 October 2014 Communicated by: Associate Editor Jan Kleissl Abstract Forecasting solar energy generation is a challenging task because of the variety of solar power systems and weather regimes encoun- tered. Inaccurate forecasts can result in substantial economic losses and power system reliability issues. One of the key challenges is the unavailability of a consistent and robust set of metrics to measure the accuracy of a solar forecast. This paper presents a suite of generally applicable and value-based metrics for solar forecasting for a comprehensive set of scenarios (i.e., different time horizons, geographic locations, and applications) that were developed as part of the U.S. Department of Energy SunShot Initiative’s efforts to improve the accuracy of solar forecasting. In addition, a comprehensive framework is developed to analyze the sensitivity of the proposed metrics to three types of solar forecasting improvements using a design-of-experiments methodology in conjunction with response surface, sen- sitivity analysis, and nonparametric statistical testing methods. The three types of forecasting improvements are (i) uniform forecasting improvements when there is not a ramp, (ii) ramp forecasting magnitude improvements, and (iii) ramp forecasting threshold changes.
    [Show full text]
  • Day-Ahead Hourly Forecasting of Power Generation From
    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Senior Member, IEEE, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri, Debora Mucci Abstract—The ability to accurately forecast power generation • Monitoring: mismatches between power forecasts and from renewable sources is nowadays recognised as a fundamental the actually generated power may be also used by energy skill to improve the operation of power systems. Despite the producers to monitor the plant operation, to evaluate the general interest of the power community in this topic, it is not always simple to compare different forecasting methodologies, natural degradation of the efficiency of the plant due and infer the impact of single components in providing accu- to the aging of some components (see [4]) or for early rate predictions. In this paper we extensively compare simple detection of incipient faults. forecasting methodologies with more sophisticated ones over 32 For the previous reasons, the topic of renewable energy photovoltaic plants of different size and technology over a whole year. Also, we try to evaluate the impact of weather conditions forecasting has been also object of some recent textbooks like and weather forecasts on the prediction of PV power generation. [5] and [6] that provide overviews of the state-of-the-art of the most recent technologies and applications of renewable Index Terms—PV plants; Machine Learning algorithms; power energy forecasting. In this context, the objective of this paper generation forecasts. is to compare different methodologies to predict day-ahead hourly power generation from PV power plants.
    [Show full text]