A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm

Abstract

Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.

A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

“…In order to reduce prediction errors, the particle swarm optimization algorithm has a strong global search ability and simple optimization, overcoming the disadvantage of the extreme learning machine model, in which the output weights are prone to random fluctuations [17,19]. A forgetting mechanism or adaptive extreme learning machine is employed to optimize the number of neurons in the hidden layer within a certain range to solve the problem of the poor generalization ability of extreme learning machines [21,87]. Due to the advantages and disadvantages of different prediction models, hybrid prediction methods are used to optimize the data processing results of different models based on specific strategies to obtain better solar PV power generation prediction results and ultimately improve predictive accuracy [92,93].…”

Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods

“…In relation to the winter season, summertime electrical power use has been higher. Principals must undertake an efficient power consumption peak management during the summer due to rising power consumption [11].…”

“…In the fourth step, an ANN with linear regression appraisal is used for socioeconomic evaluations. To assess the approach for future energy management in urban areas, the fifth phase entails developing scenarios [11].…”

A Multi-Step-Ahead Photovoltaic Power Forecasting Approach Using One-Dimensional Convolutional Neural Networks and Transformer

“…The challenge of performance degradation caused by nonlinearity between input and output, a limitation seen in statistical and physical approaches, is being addressed by machine learning-based prediction models. Machine learning techniques, such as support vector regression (SVR), decision tree (DT), and extreme learning machine (ELM), are commonly used to build prediction models [14][15][16]. However, unbalanced PV power generation patterns under varying weather conditions can negatively impact the effectiveness of machine learningbased models.…”