On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation Photovoltaic power generation;One-day-ahead forecasts;Prediction intervals;Maximum likelihood estimation;Support vector regression; The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.

To obtain accurate forecasts of photovoltaic power generation, the use of forecast datasets of meteorological elements from numerical prediction models, specifically global horizontal irradiance (GHI), is necessary. This study seeks to validate, and therefore improve GHI forecasts. Ground-based data from Japan Meteorological Agency (JMA) stations are used in a JMA mesoscale model (MSM) during the time period from 2008 to 2012 and temporal and spatial characteristics of forecast errors are analyzed. Statistical monthly evaluations show that associated errors vary between seasons, with monthly GHI mean bias error values ranging from −60 to +45W/m2 and root mean square errors (RMSEs) ranging from 95 to 170W/m2. Mapping of forecast errors show that underestimation of GHI forecast values and large RMSE values are significant in the southern part of Japan (a subtropical region located along the Pacific Ocean), particularly during summers. In winter, overestimation of GHI forecasts is found throughout the entire Japanese archipelago. The frequency of different cloud type occurrences over the Japanese islands indicate that regional and seasonal variations in cloud types are related to relatively large GHI forecast errors. High-level cirrus clouds, mid-level altocumulus, and low-level stratus are often observed during summer, when forecasted values are underestimated, and during winter, when values are overestimated