Machine Learning Based PV Power Generation Forecasting in Alice Springs

Abstract

The generation volatility of photovoltaics (PVs) has created several control and operation challenges for grid operators. For a secure and reliable day or hour-ahead electricity dispatch, the grid operators need the visibility of their synchronous and asynchronous generators' capacity. It helps them to manage the spinning reserve, inertia and frequency response during any contingency events. This study attempts to provide a machine learning-based PV power generation forecasting for both the short and longterm. The study has chosen Alice Springs, one of the geographically solar energy-rich areas in Australia, and considered various environmental parameters. Different machine learning algorithms, including Linear Regression, Polynomial Regression, Decision Tree Regression, Support Vector Regression, Random Forest Regression, Long Short-Term Memory, and Multilayer Perceptron Regression, are considered in the study. Various comparative performance analysis is conducted for both normal and uncertain cases and found that Random Forest Regression performed better for our dataset. The impact of data normalization on forecasting performance is also analyzed using multiple performance metrics. The study may help the grid operators to choose an appropriate PV power forecasting algorithm and plan the time-ahead generation volatility.INDEX TERMS Artificial intelligence, machine learning, power systems, PV power forecasting, renewable energy, statistical regression I. INTRODUCTION A. OVERVIEW

A Review of Machine Learning-Based Photovoltaic Output Power Forecasting: Nordic Context

“…Comparative study of the performance of PV power prediction models in all the forecast horizon categories has received limited attention in the literature. This issue has been addressed by recent work of [53]. Here the authors evaluated the performance of various ML algorithms such as LR (Linear Regressor), PR (Polynomial Regressor), DTR (Decision Tree Regressor), SVR, RFR (Random Forest Regressor), MLP, and LSTM for short-term (24 hours ahead), medium-term (1 week ahead), and long-term forecast (1 year ahead).…”

On the advantages of P2P ML on mobile devices

“…As a matter of fact, machine-learning algorithms are traditionally used to generate forecasting models for demand [35][36][37] and generation [38][39][40] in energy field. However, the topic of estimating the energy consumption of machine-learning algorithms has not yet been studied adequately in the literature [41].…”

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

“…Although the model generalization to complex cases, the MLP model is restricted to reveal patterns among sequential samples such as PV generation data. This is mainly due to the fact that this configuration does not save the previous information in an internal memory [43]. Consequently, the Time Series (TS) data are trained independently, which may lead to poor accuracy [43].…”

“…This is mainly due to the fact that this configuration does not save the previous information in an internal memory [43]. Consequently, the Time Series (TS) data are trained independently, which may lead to poor accuracy [43].…”