Taxonomy research of artificial intelligence for deterministic solar power forecasting

Fundamentals and business model for resource aggregator of demand response in electricity markets

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. 

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.

A review and taxonomy of wind and solar energy forecasting methods based on deep learning

A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting

A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework

This article proposes a meta-heuristic optimization-based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction; 2) a good clustering should achieve a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (DBSCAN) is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

Meta-Heuristic Optimization-Based Two-Stage Residential Load Pattern Clustering Approach Considering Intra-Cluster Compactness and Inter-Cluster Separation

Solar irradiance is one of the most crucial factors affecting the photovoltaic (PV) power output. Accurate solar irradiance forecasting can help predict future PV power generation for grid managers, thus to mitigate the adverse effect of random PV power on the power grid. However, research on real-time surface irradiance distribution in a certain region still not be presented in previous studies, which can bring further convenience to solar power forecasting and power grid dispatching work. In this paper, a surface irradiance distribution mapping model for PV power station based on ground-based sky images is proposed. First, we process raw sky image with distortion rectification and background message dislodged. Then after cloud motion displacement calculation by hybrid Fourier phase correlation (HFPC) theory, we extract the gray level and position information of pixels in sky images to explore the mapping relationship between sky image and solar irradiance. Subsequently, an adaptive sky image-irradiance mapping model is built, trained, and updated according to real-time sky images and solar irradiance. Simulation results show that the proposed method in relation to forecast can achieve real-time surface irradiance distribution mapping at quite high accuracy.

Min Shi

Papers

A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework

Meta-Heuristic Optimization-Based Two-Stage Residential Load Pattern Clustering Approach Considering Intra-Cluster Compactness and Inter-Cluster Separation

Surface Irradiance Distribution Mapping Model for Photovoltaic Power Station Based on Ground-based Sky Images