Deep learning approach to forecasting hourly solar irradiance

Abstract: In this dissertation, six artificial intelligence (AI) based methods for forecasting solar irradiance are presented. Solar energy is a clean renewable energy source (RES) which is free and abundant in nature. But despite the environmental impacts of fossil energy, global dependence on it is yet to drop appreciably in favor of solar energy for power generation purposes. Although the latest improvements on the technologies of photovoltaic (PV) cells have led to a significant drop in the cost of solar panels, solar power is still unattractive to some consumers due to its unpredictability. Consequently, accurate prediction of solar irradiance for stable solar power production continues to be a critical need both in the field of physical simulations or artificial intelligence. The performance of various methods in use for prediction of solar irradiance depends on the diversity of dataset, time step, experimental setup, performance evaluators, and forecasting horizon. In this study, historical meteorological data for the city of Johannesburg were used as training data for the solar irradiance forecast. Data collected for this work spanned from 1984 to 2019. Only ten years (2009 to 2018) of data was used. Tools used are Jupyter notebook and Computer with Nvidia GPU...M.Ing. (Electrical and Electronic Engineering Management

Photovoltaic forecasting with artificil neural networks

Tese de mestrado em Engenharia da Energia e do Ambiente, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2014São necessários esforços adicionais para promover a utilização de sistemas de produção de energia fotovoltaica conectados à rede como uma fonte fundamental de sistemas de energia elétrica, em níveis de penetrações mais elevados. Nesta tese é abordada a variabilidade da geração elétrica por sistemas fotovoltaicos e é desenvolvida com base na premissa de que o desempenho e a gestão de pequenas redes elétricas podem ser melhorados quando são utilizadas as informações de previsão de energia solar. É implementado um sistema de arquitetura de rede neuronal para o modelo auto-regressivo não-linear com variáveis exógenas (NARX) utilizando, não só, dados meteorológicos locais, mas também medições de sistemas fotovoltaicos circunjacentes. Diferentes configurações de entrada são otimizadas e comparadas para avaliar os efeitos no desempenho do modelo para previsão. A precisão das previsões revelou melhoria quando lhe são adicionadas informações de sistemas fotovoltaicos circunjacentes. Após ser selecionada a configuração de entrada da rede com o melhor desempenho, são testadas previsões com várias horas de antecedência e comparadas com o modelo da persistência, para verificar a precisão do modelo na previsão de diferentes horizontes temporais de curto prazo. O modelo NARX superou, claramente, o modelo de persistência, resultando num RMSE de 3,7% e de 4,5% aquando da antecipação das previsões de 5min e 2h30min, respetivamente.Additional efforts are required to promote the use of grid-connected photovoltaic (PV) systems as a fundamental source in electric power systems at the higher penetration levels. This thesis addresses the variability of PV electric generation and is built based on the premise that the performance and management of small electric networks can be improved when solar power forecast information is used. A neural network architecture system for the Nonlinear Autoregressive with eXogenous inputs (NARX) model is implemented using not only local meteorological data but also measurements of neighbouring PV systems. Input configurations are optimized and compared to assess the effects in the model forecasting performance. The added value of the information of the neighbouring PV systems has demonstrated to further improve the prediction accuracy. After selecting the input configuration with the best network performance, forecasts up to several hours in advance are tested to verify the model forecasting accuracy for different short-term time horizons and compared with the persistence model. The NARX model clearly outperformed the persistence model and yielded a 3.7% and a 4.5% RMSE for the anticipation of the 5min and 2h30 forecasts, respectively

GIS and Remote Sensing for Renewable Energy Assessment and Maps

This book aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making

Assessment of Renewable Energy Resources with Remote Sensing

The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii Fernando Ramos Martins Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1 André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7 Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33 Joaquín Alonso-Montesinos Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43 Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61 Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79 Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101 Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125 Ian R. Young, Ebru Kirezci and Agustinus Ribal The Global Wind Resource Observed by Scatterometer Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147 Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura Coastal Wind Measurements Using a Single Scanning LiDAR Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165 Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189 Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma

Residential Demand Response using Electricity Smart Meter Data

The electricity industry is currently undergoing changes in a transitioning period characterised by Energy 3D: Digitalisation, Decentralisation, and Decarbonisation. Smart meters are the vital infrastructure necessary to digitalise the energy system as well as enable advancements in decentralisation and decarbonisation. As of today, more than 500 million smart meters have been installed worldwide, with that number expected to rise to several billion installations over the decade. Smart meters enable electricity load to be measured with half-hourly granularity, providing an opportunity for demand-side management innovations that are likely to be advantageous for both utility companies and customers. Among these innovations, time-of- use (TOU) tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector, however actual use is still limited. The objective of this thesis is to investigate opportunities and problems related to TOU tariffs utilising smart meter data at the national level. The authors have identified four major research gaps which need to be filled in order to expand commercial applications of TOU tariffs. These gaps are the described and addressed in the following chapters: the "TOU load adaptation forecasting problem", the "TOU winner detection problem", the "TOU public dataset problem", and the "excess generation forecasting problem". This thesis demonstrates three modelling approaches and one new TOU dataset (CAMSL). A significant contribution to the field is through the discover of new summary statistical features (statistical moments) and assesses the capacity of these to encapsulate other more widely used explanatory variables of demand response. The thesis is concluded by discussing future works and policy implications, such as the necessity of the more tailored modelling works and public live-stream of smart meter data, which could accelerate the roll-out of the demand side management at the residential sector.EPC

Multi-authored monograph

Unmanned aerial vehicles. Perspectives. Management. Power supply : Multi-authored monograph / V. V. Holovenskiy, T. F. Shmelova,Y. M. Shmelev and oth.; Science Editor DSc. (Engineering), T. F. Shmelova. – Warsaw, 2019. – 100 p. - ISBN 978-83-66216-10-5.У монографії аналізуються можливі варіанти енергопостачання та управління безпілотними літальними апаратами. Також розглядається питання прийняття рішення оператором безпілотного літального апарату при управлінні у надзвичайних ситуаціях. Рекомендується для фахівців, аспірантів і студентів за спеціальностями 141 - «Електроенергетика, електротехніка та електромеханіка», 173 - «Авіоніка» та інших суміжних спеціальностей.The monograph analyzes the possible options for energy supply and control of unmanned aerial vehicles. Also, the issue of decision-making by the operator of an unmanned aerial vehicle in the management of emergencies is considered.

SVR-based model to forecast PV power generation under different weather conditions

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation

Clustering analysis for classification and forecasting of solar irradiance in Durban, South Africa.

Doctor of Philosophy. University of KwaZulu-Natal, Durban. 2017.Classification and forecasting of solar irradiance patterns has become increasingly important for operating and managing grid-connected solar power plants. A powerful approach for classification of irradiance patterns is by clustering of daily profiles, where a profile is defined as irradiance as a function of time. Classification is useful for forecasting because if the class of a day can be successfully forecast, then the irradiance profile of that day will share the general pattern of the class. In Durban, South Africa (29.871 °S; 30.977 °E), beam and diffuse irradiance profiles were recorded over a one-year period and normalized to a clear sky model to reduce the effect of seasonality, from which several variables were derived, namely minute-resolution beam, hourly-resolution beam and diffuse, and hourly-resolution beam variability. To these variables, individually and in combination, k-means clustering was applied, and beam irradiance was found to be the one that best distinguishes between sky conditions. In particular, clustering of hourly-resolution beam irradiance produced four classes with diurnal patterns characterized as sunny all day, cloudy all day, sunny morning-cloudy afternoon, and cloudy morning-sunny afternoon. These classes were then used to forecast beam and diffuse irradiance for the day ahead, in association with cloud cover forecasts from Numerical Weather Prediction (NWP) output. Two forecasting methods were investigated. The first used k-means clustering on predicted daily cloud cover percentage profiles from the NWP, which was a novel aspect of this research. The second used a rule whereby predicted cloud cover profiles were classified according to whether their averages in the morning and afternoon were above or below 50%. From both methods, four classes were obtained that had diurnal patterns associated with the irradiance classes, and these were used to forecast the irradiance class for the day ahead. The two methods had a comparable success rate of about 65%. In addition, hour-ahead forecasts of beam and diffuse irradiance were performed by using the mean profile of the forecast irradiance class to extrapolate from the current measured value to the next hour. The method showed an average improvement of about 22% for beam and diffuse irradiance over persistence forecasts. These results suggest that classification of predicted cloud cover and irradiance profiles are potentially useful for development of class-specific, multi-hour irradiance forecast models

Thermal Behaviour, Energy Efficiency in Buildings and Sustainable Construction

This Special Issue includes 20 contributions from across the world with very interesting and current research topics, such as insulation solutions and CO2 emissions; thermal transmittance of LSF walls; statistics for China’s building energy consumption; natural ventilation; thermal behavior of an earthbag building; thermal performance and comfort in a vernacular building; overheating risk under future extreme weather conditions; analytical methods to estimate the thermal transmittance of LSF walls; model simplification on energy and comfort simulation analysis; Trombe wall thermal behavior and energy efficiency of an LSF compartment; new metering hot box for in situ hygrothermal measurement; mechanical and thermal performance of compressed earth blocks; life-cycle assessment of a new house; energy analyses of Serbian buildings with horizontal overhangs; thermal properties of mortar blocks by using recycled glass; prediction of cooling energy consumption building using machine learning techniques; occupants’ behavior, climate change, heating, and cooling energy needs of buildings; a new method for establishing a hygrothermally controlled test room; nonintrusive measurements to incorporate the air renovations in dynamic models; and retrofit of existing buildings with aerogel panels