Multitasking recurrent neural network for photovoltaic power generation prediction

With the increased uptake of renewable energy resources (RES), power generation from Photovoltaics (PV) cells is gaining momentum and heavily depends on environmental and meteorological conditions. Predicting PV power generation plays an important role in power management and dispatch optimization. Due to the scales of PV systems, the generation may vary significantly, which is difficult to have accurate PV power generation prediction over different categories of customers (e.g., residential, agricultural, industrial, and commercial) using a single model. In this paper, we define the PV power generation prediction as a multitasking prediction problem, where PV generation over each of the categories is modeled as a separate prediction task. To address this problem, we employ a recurrent neural network (RNN) as the predictor for each prediction task and propose a multitasking RNN (MT-RNN) framework. Instead of addressing each task individually, MT-RNN performs knowledge transfer among different tasks to improve the prediction accuracy of each task, where the knowledge is represented via connection weights and biases in each RNN. In comparison to several state-of-art deep neural network (DNN) models that solve each task individually, the superior performance of MT-RNN in terms of prediction accuracy is demonstrated. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under theCCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

With the increasing importance of renewable energy, predicting photovoltaic (PV) power generation becomes crucial for power management and optimization. This paper proposes a multitasking prediction approach using recurrent neural networks (RNNs) to improve the accuracy of PV power generation prediction across different customer categories. The proposed multitasking RNN (MT-RNN) framework transfers knowledge among tasks, achieving superior performance compared to individual deep neural network (DNN) models.