Predicting power output in natural gas-fired peaker power plants is critical due to their intermittent operation and high economic significance. These plants, which supply power sporadically and at a premium price, pose a unique challenge for grid operators. We aim to forecast power output based on environmental conditions, enabling more informed decisions about plant activation and power purchasing. Exploratory Data Analysis will precede regression modeling using Apache Spark ML Pipeline to achieve this predictive capability.