The alarming rise in global surface temperatures calls for an urgent shift towards renewable sources for power generation. According to the 2021 global road map for Sustainable Development Goal 7, i.e., affordable and clean energy, and the Paris agreement on climate change, there is a need to triple the global renewable power capacity by 2030 and reach net zero emissions by 2050. This increase incapacity also requires an improvement in the accuracy of renewable power forecasting. A reliable power forecast is essential to reduce operational costs and improve the power grid’s safety and maintenance. Renewable power generation forecasts using machine learning are typically implemented as single-tasklearning (STL) models, where a separate model is trained for each solar or wind park. In recent years, transfer learning is gaining popularity in these systems, as it can be used to transfer the knowledge gained from source parks to a target park. However, in transfer learning, there is a need to determine the most similar source park(s) among the existing parks. This similarity determination using historicalpower measurements is challenging when the target park has limited to no historical data samples. Therefore, we propose a simple multi-task learning (MTL) architecture that initially learns a common representation of input weather features among the source tasks using a Unified Autoencoder (UAE) and then learns the task-specific information utilizing a Task Embedding (TE) layer in a neural network.