Recently, the world has been transitioning towards renewable energy (RE) consumption. The smart grid, a pivotal factor in facilitating this transition, is an electricity network that integrates digital and information communication technologies to enhance the capacities of devices (e.g., smart metres, sensors, and automated control systems). It enables efficient communication, monitoring, control, and management of electricity within the network. Additionally, the smart grid ensures proper electricity generation and consumption, particularly when addressing the intermittency of RE sources like solar energy.
Accordingly, solar forecasting is one of the most critical components of renewable energy systems. The smart grid system relies on resilience and accuracy in power generation and consumption, and solar forecasting enhances the ability to predict the quantity of solar energy generated in advance. Solar energy production is highly volatile, depending on factors such as weather conditions and time of day. Accurate solar forecasting allows electricity operators to manage electrical loads effectively, reduce the risk of mismatched power generation and consumption, and improve the overall efficiency of the electrical power system.
Developing accurate solar forecasting requires diverse and high-quality datasets. These include specific weather data for a given area (e.g., temperature, humidity, wind speed, and cloud cover), technical details about the solar system (e.g., solar panel size, panel efficiency, and installation specifications), and information from the electricity network (e.g., power allocation data).
However, collecting datasets for forecasting models poses challenges. The data are often scattered across multiple sources, presented in varying formats and types, and serve different purposes. For instance, policymakers require an overview of the data to inform strategy development, while forecasting staff or researchers need detailed, specific information to improve accuracy. Meanwhile, the general public seeks simplified and easily understandable reports. These datasets may come in formats such as Excel, CSV, or PDF. These challenges are global in nature and significantly impact the quality and effectiveness of forecasting outcomes.
Therefore, standardising the collected datasets is essential for effective Big Data analysis and the adoption of artificial intelligence (AI). Without proper data management, analysis and AI integration cannot deliver the desired results.
Big Data and AI are pivotal tools for advancing solar forecasting. They can analyse large datasets from various sources, such as satellites, weather monitoring stations, and solar systems used by general consumers, providing a comprehensive view of power generation and consumption. They also enable the development of highly accurate forecasts. An Open Data Platform is another promising tool. This platform provides open access to solar energy-related information, such as data collected from household solar panel sensors, improving both local and national forecasting accuracy and fostering collaboration among public, private, and third-sector stakeholders.
However, the successful development of Big Data systems and Open Data Platforms requires consistent technological infrastructure and supportive policies that ensure safe and equitable information sharing. Such measures are crucial to maximising the benefits of solar forecasting development and creating a sustainable renewable energy system for the country.
Authors: Peetiphat Thirakiat, Varinthon Kessayom and Dr Siripha Junlakarn, Energy Research Institute (ERI), Chulalongkorn University
This article was originally published on greennews.agency as part of the project Clean, Affordable, and Secure Energy for Southeast Asia (CASE).
