Optimization Of Solar Energy Systems Research Articles

Advancing solar energy forecasting with modified ANN and light GBM learning algorithms

<abstract> <p>In the evolving field of solar energy, precise forecasting of Solar Irradiance (SI) stands as a pivotal challenge for the optimization of photovoltaic (PV) systems. Addressing the inadequacies in current forecasting techniques, we introduced advanced machine learning models, namely the Rectified Linear Unit Activation with Adaptive Moment Estimation Neural Network (RELAD-ANN) and the Linear Support Vector Machine with Individual Parameter Features (LSIPF). These models broke new ground by striking an unprecedented balance between computational efficiency and predictive accuracy, specifically engineered to overcome common pitfalls such as overfitting and data inconsistency. The RELAD-ANN model, with its multi-layer architecture, sets a new standard in detecting the nuanced dynamics between SI and meteorological variables. By integrating sophisticated regression methods like Support Vector Regression (SVR) and Lightweight Gradient Boosting Machines (Light GBM), our results illuminated the intricate relationship between SI and its influencing factors, marking a novel contribution to the domain of solar energy forecasting. With an R<sup>2</sup> of 0.935, MAE of 8.20, and MAPE of 3.48%, the model outshone other models, signifying its potential for accurate and reliable SI forecasting, when compared with existing models like Multi-Layer Perceptron, Long Short-Term Memory (LSTM), Multilayer-LSTM, Gated Recurrent Unit, and 1-dimensional Convolutional Neural Network, while the LSIPF model showed limitations in its predictive ability. Light GBM emerged as a robust approach in evaluating environmental influences on SI, outperforming the SVR model. Our findings contributed significantly to the optimization of solar energy systems and could be applied globally, offering a promising direction for renewable energy management and real-time forecasting.</p> </abstract>

Forecasting solar energy production: A comparative study of machine learning algorithms

The use of solar energy has been rapidly expanding as a clean and renewable energy source, with the installation of photovoltaic panels on homes, businesses, and large-scale solar farms. The increasing demand for sustainable energy sources has pushed the growth of the solar industry, as well as advancements in technology, making solar panels more efficient and cost-effective. The implementation of solar energy not only reduces our reliance on non-renewable fossil fuels but also helps to mitigate the effects of climate change by reducing carbon emissions. This paper presents a complete and comparative study of solar energy production forecasting in Morocco using six machine learning (ML) algorithms : Support Vector Regression (SVR), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Generalized Additive Model (GAM) and Extreme Gradient Boosting (XGBOOST), based on Solar Power Plant daily data installed in Benguerir city of Morocco between January and December 2022. The models were trained, tested, and then evaluated. In order to assess the models performance four metrics were used in this study, namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE)and R-squared (R2). The performance of the models reveals ANN to be the most effective predictive model for energy forecasting in similar cases with the lowest value of RMSE, MSAE and the highest value of R-squared, which are accepted as one of the most important performance criteria by the ANN model. The findings of this study not only validate the effectiveness of the ANN algorithm but also offer the appropriate parameters for achieving the best results in predicting solar energy production. By identifying the optimal configuration of the ANN algorithm, we provide valuable insights that can be directly applied in real-world applications, thereby enhancing the optimization of solar energy systems and contributing to a sustainable future, particularly the integration of these results in an edge device for the predictive maintenance of photovoltaic power plants.

Effective battery charging system using step voltage and step duty size-based MPPT controller for solar PV system

Solar energy is an excellent source of renewable energy, despite its intermittent nature that can pose a challenge. To meet load demand, a converter is required to integrate the system. The converter acts based on control signals from the controller, which is trained according to the end demand and availability of Sun Irradiance. This paper utilizes the Incremental Conductance (IC) and Perturb and Observe (P&O) algorithms, which are widely accepted in the industry and easy to implement. This study aims to design and compare a Step Voltage (SV) controller and a Step-Duty (SD) Maximum Point Tracking (MPPT) IC controller-based DC to DC boost converter. The paper compares the performance of SV and SD controller-based DC to DC boost converters under different environmental conditions, evaluates the system’s effectiveness by comparing the oscillations in load power for both conditions and discusses the impact of battery charging on the Load. The system performance is tested using MATLAB Simulink/coding, considering the Indian solar radiation intensity (SRI) scenario and temperature variations. Overall, this study provides a comprehensive analysis of the performance of the proposed system, which can contribute to the development and optimization of solar energy systems in various applications. From the comparative analysis of IC SD, SV and P&O SD, SV it is observed the performance of IC SD is superior. The impact of battery charging using IC SD controller on the load and MPPT point is also discussed.

Multi-objective optimization of solar energy systems for electricity and hot water generation in collective residential buildings considering the power-to-heat concept

An extremely important question concerning the planning and installation of solar systems in residential buildings is how to optimally size solar photovoltaic (PV) systems and solar water heating systems. This question is challenging especially if there is an interest to explore the excess PV generation to produce of portion of the thermal load and assist the solar water heating system under the so-called power-to-heat concept. A tri-objective optimization process is introduced to find the best size of solar PV panels, thermal collectors and storage capacity for a Moroccan collective residential building. Modelling and optimization investigations rely on high-resolution time step over a one year operation, considering pertinent life-cycle objective functions which are the Levelized Cost of Electricity (LCOE), Levelized Cost of Heat (LCOH) and the Renewable Energy Penetration (REP). The collective residential building is located in Fez, Morocco. Instantaneous variations of PV power generation, thermal collector efficiencies, water temperature in the storage tank, auxiliary power and power imports from the grid are determined at each time step. The developed numerical tool can be used in other buildings categories and in other locations revealing a huge interest in improving the feasibility and competitiveness of solar energy systems in buildings. For total annual electric and thermal loads of 96.31 MWh/year and 32.88 MWhth/year, respectively, the optimum design consisted on 25 kWp PV capacity, 18 m2 of thermal collectors and 1.8 m3 of thermal storage. The corresponding LCOE, LOCH and REP are determined to be 17.8 c$/kWh, 3.5 c$/kWh and 45.8%, respectively. The self-sufficiency ratio, self-consumption ratio and solar thermal fraction for the final optimum design are 30.6%, 69.7% and 90.1%, respectively.

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

Evaluation of some mathematical models of solar radiation received by a ground collector

Purpose Using modeling approaches, this paper aims to propose different mathematical models for estimating the different components of the solar radiation as well as the received solar energy by a collector. Design/methodology/approach In this article, the authors consider three mathematical models to estimate the solar radiation captured at ground level by a solar collector. These models are Capderou model, Liu & Jordan model and R.sun model. In the context of the design of experiments, we performed measurements of solar radiation received by a collector using a pyranometer. The obtained measurements were compared with the three mathematical models. Findings The comparison enabled the subsequent evaluation to determine the most appropriate model that best fit for our region. As a result, the Capderou model reveals to be the most suitable for our region. Originality/value Estimation of solar radiation at ground level (received by a collector) is of paramount importance for the design and optimization of solar energy systems. Nevertheless, many factors influence the amount of energy received by a collector situated at a ground, such as the longitude of the location, latitude, altitude, tilt collector orientation, temperature and humidity of the environment, wind speed, etc. Because of the complex influence of these parameters, the received solar radiation by the collector is a dynamical and a random process.

Thermodynamic optimization of a solar system for cogeneration of water heating and absorption cooling

This paper presents a contribution to understanding the behavior of solar-powered air conditioning and refrigeration systems with a view to determining the manner in which refrigeration rate, mass flows, heat transfer areas, and internal architecture are related. A cogeneration system consisting of a solar concentrator, a cavity-type receiver, a gas burner, and a thermal storage reservoir is devised to simultaneously produce heat (hot water) and cooling (absorption refrigerator system). A simplified mathematical model, which combines fundamental and empirical correlations, and principles of classical thermodynamics, mass and heat transfer, is developed. The proposed model is then utilized to simulate numerically the system transient and steady-state response under different operating and design conditions. A system global optimization for maximum performance (or minimum exergy destruction) in the search for minimum pull-down and pull-up times, and maximum system second law efficiency is performed with low computational time. Appropriate dimensionless groups are identified and the results are presented in normalized charts for general application. The numerical results show that the three-way maximized system second law efficiency, ηII,max,max,max, occurs when three system characteristic mass flow rates are optimally selected in general terms as dimensionless heat capacity rates, i.e. (ψss, ψwxwx, ψHs)opt=(0.335, 0.28, 0.2). The minimum pull-down and pull-up times, and maximum second law efficiencies found with respect to the optimized operating parameters are sharp and, therefore, important to be considered in actual design. As a result, the model is expected to be a useful tool for simulation, design, and optimization of solar energy systems in the context of distributed power generation. Copyright © 2008 John Wiley & Sons, Ltd.