Solar Photovoltaic Power Forecasting: A Review

Abstract

The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

“…The forecasting results aligned with industry standards for real-time and day-ahead scenarios. Accordingly, the error was generally around 5-7% for one-to-two-hour forecasting [56][57][58] and should not exceed a 20% RMSE for forecasting one to many hours [70,71].…”

The design of distributed photovoltaic charging station for electric vehicles

“…(Nallolla et al, 2022) aim to design the optimal hybrid renewable energy system, wherein the design consists of PV/BS (1476 kW-solar PV, 417 batteries, electrolyser-200 kW, hydrogen tank-20 kg and 59.6 kW-converter) by comparing the minimum net present cost (NPC: $7.01 M), levelized cost of energy (LCOE: 0.244 $/kWh), and the high renewable fraction (RF: 84.1%) [13]. (Iheanetu, 2022) provide a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures [16]. In order to improve the efficiency of photovoltaic panels, a photovoltaic-temperature difference (PV-TE) hybrid power generation system can be formed by combining photovoltaic power generation with the thermoelectric generation, which is based on the Seebeck effect.…”

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

“…This context underscores the necessity for a diversified energy strategy that includes not only renewable sources such as solar but also the reliable capacity of nuclear power. In order to ensure a sustainable energy future, it is critical to improve the reliability and accuracy of solar PV power forecasting [7]. This forecasting will facilitate the integration of solar power into power grids, complementing stable sources such as nuclear power.…”