The urgent need to reduce carbon dioxide emissions in energy generation poses a significant challenge for modern society. Consequently, it is of paramount importance to intensify efforts to enhance clean energy sources, such as wind energy. In this context, addressing the inherent variability of wind power through prediction methodologies is a compelling field of study. Additionally, this clean energy should be accessible to everyone, even on a small scale, by leveraging affordable devices like the Raspberry Pi and other low-cost hardware platforms. This study aims to evaluate the effectiveness of machine learning (ML) algorithms, with a particular focus on deep learning models, in accurately forecasting wind turbine power output. Specifically, the research uses Long Short-Term Memory (LSTM) networks that are designed and trained using conventional computing resources, but evaluated on an affordable computing system like the Raspberry Pi 3 with 2 GB of RAM to facilitate the management of energy generation. Through a comparative analysis, considering precision and real-time performance, the study identifies the optimal parameters for accurately modeling time series data related to wind energy production and evaluates its implementation. Our findings demonstrate that effective wind power forecasting can be achieved on low-cost hardware platforms, highlighting the potential for widespread adoption and personal management of wind energy generation.