A Very Short-Term Energy Forecasting Technique for Small Scale Photovoltaic Systems using k-Nearest Neighbour Algorithm

The field of photovoltaic (PV) forecasting has been evolving rapidly in the recent years. This paper provides a very short-term forecasting technique on energy harvested from small scale PV systems. It makes use of a supervised machine learning technique, k-nearest neighbours (kNN), to provide PV owners 5 easily-comprehensible output levels of Very Low, Low, Medium, High and Very High. This proposed technique uses readily available data from inverters, namely time of the day and instantaneous power, and data from commonly used additional weather measurement equipment namely outdoor temperature, panel temperature and on-site irradiance. The proposed technique targets very short-term forecasting period of 15 minutes ahead, which is sufficient for building owners to activate alternatives such as powering up backup generator or switching off non-critical loads to reduce load demand. The short-term forecasting results are useful in small localized areas where the weather changes very quickly. Its results can be passed to smart energy management system to aid in their decision makings. Historical data of an existing 30kWp PV system located in Singapore is used to evaluate the accuracy of the kNN short term forecasting technique. Despite the lack of expensive and complicated resources such as numerical weather prediction models and satellite and sky imagery observations of clouds, the proposed technique achieved an acceptable accuracy of over 68%. The paper compares and discusses the parameters and the number of neighbours to be used in the technique.