Forecasting the location of electrical activity at a very short time range remains one of the most challenging predictions to make, primarily attributable to the chaotic nature of thunderstorms. Additionally, the punctual nature of lightning further complicates the establishment of reliable forecasts. This article introduces StrikeNet, a specialized Convolutional Neural Network (CNN) model designed for very short-term forecasts of electrical activity locations, utilizing sequences of temporal images as input and only two data types. Employing soft Non-Maximum Suppression (NMS) techniques, incorporating morphological features within residual blocks, and implementing dropout regularization, StrikeNet is specifically designed for detecting and predicting pixel-sized objects in images. This design seamlessly aligns with the task of forecasting imminent electrical activity giving great scores of 0.42 for precision, 0.78 for detection, and an F1-Score about 0.54.