  The increasing installation of Photovoltaics (PV) cells leads to more
generation of renewable energy sources (RES), but results in increased
uncertainties of energy scheduling. Predicting PV power generation is important
for energy management and dispatch optimization in smart grid. However, the PV
power generation data is often collected across different types of customers
(e.g., residential, agricultural, industrial, and commercial) while the
customer information is always de-identified. This often results in a
forecasting model trained with all PV power generation data, allowing the
predictor to learn various patterns through intra-model self-learning, instead
of constructing a separate predictor for each customer type. In this paper, we
propose a clustering-based multitasking deep neural network (CM-DNN) framework
for PV power generation prediction. K-means is applied to cluster the data into
different customer types. For each type, a deep neural network (DNN) is
employed and trained until the accuracy cannot be improved. Subsequently, for a
specified customer type (i.e., the target task), inter-model knowledge transfer
is conducted to enhance its training accuracy. During this process, source task
selection is designed to choose the optimal subset of tasks (excluding the
target customer), and each selected source task uses a coefficient to determine
the amount of DNN model knowledge (weights and biases) transferred to the aimed
prediction task. The proposed CM-DNN is tested on a real-world PV power
generation dataset and its superiority is demonstrated by comparing the
prediction performance on training the dataset with a single model without
clustering.
