Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction

TL;DR

This paper introduces a clustering-based multitasking deep neural network for PV power prediction, improving accuracy by leveraging customer-type clustering and knowledge transfer across models.

Contribution

It proposes a novel CM-DNN framework that clusters PV data by customer type and transfers knowledge between models to enhance prediction accuracy.

Findings

CM-DNN outperforms single-model approaches in PV power prediction.

Clustering improves model specialization for different customer types.

Knowledge transfer enhances prediction accuracy across tasks.

Abstract

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.