VIPOC project research summary (discussion paper)

Predicting the output power of renewable energy production plants distributed on a wide territory is a valuable goal, both for marketing and energy management purposes. In this paper, we describe Vi-POC (Virtual Power Operating Center) - a distributed system for storing huge amounts of data, gathered from energy production plants and weather prediction services. Due to the heterogeneity and the high volume of data, it is necessary to exploit suitable Big Data analysis techniques in order to perform a quick and secure access to data that cannot be obtained with traditional approaches for data management. We use HBase over Hadoop framework on a cluster of commodity servers in order to provide a system that can be used as a basis for running machine learning algorithms. We perform one-day ahead forecast of PV energy production based on Artificial Neural Networks in two learning settings - structured and non-structured output prediction. Preliminary experimental results confirm the validity of the approach, also when compared with a baseline approach.