  The Sun continuously affects the interplanetary environment through a host of
interconnected and dynamic physical processes. Solar flares, Coronal Mass
Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key
drivers of space weather in the near-Earth environment and beyond. While some
CMEs and flares are associated with intense SEPs, some show little to no SEP
association. To date, robust long-term (hours-days) forecasting of SEP
occurrence and associated properties (e.g., onset, peak intensities) does not
effectively exist and the search for such development continues. Through an
Operations-2-Research support, we developed a self-contained model that
utilizes a comprehensive dataset and provides a probabilistic forecast for SEP
event occurrence and its properties. The model is named Multivariate Ensemble
of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP).
MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests
a comprehensive dataset (MEMPSEP III - (Moreland et al., 2023)) of full-disc
magnetogram-sequences and in-situ data from different sources to forecast the
occurrence (MEMPSEP I - this work) and properties (MEMPSEP II - Dayeh et al.
(2023)) of a SEP event. This work focuses on estimating true SEP occurrence
probabilities achieving a 2.5% improvement in reliability and a Brier score of
0.14. The outcome provides flexibility for the end-users to determine their own
acceptable level of risk, rather than imposing a detection threshold that
optimizes an arbitrary binary classification metric. Furthermore, the
model-ensemble, trained to utilize the large class-imbalance between events and
non-events, provides a clear measure of uncertainty in our forecast
