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The grid-connected photovoltaic (PV) power stations are instability and volatility due to meteorological factors. A way to improve this problem is PV power forecasting. This paper proposed an improved short-term PV power prediction model that combines an extreme learning machine (ELM) neural network and similar day method. Firstly, a narrow-band Internet of Things (NB-IoT) intelligent combiner box data monitoring system is designed to collect multivariate meteorological factors and original PV output power datasets in different seasons. Secondly, the corresponding training set is selected according to the season type of the forecast day, and the Euclidean distance (ED) between the training set and the forecasting day is calculated, and the M-day with a small Euclidean distance is selected. Then, the N-day similar day data is divided among the M days as the new training set input, and the P-day optimal similar day data and the multivariate meteorological of the prediction day are divided as test set inputs. Finally, the ELM neural network prediction model is used to predict the output power of the predicted day. The experimental results show that the proposed method has the highest prediction accuracy in contrast to other two prediction models.

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