PhD & Postdoc

Olumide Adeleke

Smart Grid Analytics · University of Lagos

Olumide Adeleke develops federated machine learning models for non-technical loss detection — i.e., electricity theft — in Nigeria's distribution grid, where aggregate annual losses exceed 40% of billed energy in Lagos state alone. At the University of Lagos's Electrical Engineering department, supervised by Prof. Tunde Oladapo, he formulates the detection problem as temporal anomaly identification in 15-minute interval smart meter consumption patterns, using a long short-term memory autoencoder trained in a privacy-preserving federated learning configuration so that raw household data never leaves each distribution transformer's edge computing node. The federated averaging protocol across 12 distribution transformer nodes in a Lagos pilot area converges in 22 communication rounds to within 3% of a centralised training baseline, while the privacy guarantee prevents customer data exposure. The model achieves a precision of 0.82 and recall of 0.76 for flagging meter ID–substation association anomalies confirmed by follow-up field audits — corresponding to a 18% higher detection rate than the current visual inspection programme. A consulting engagement with Eko Electricity Distribution Company (EKEDC) provides both the meter data infrastructure and the field audit validation resources, and Olumide's model has been approved for a 2026 citywide rollout covering 400,000 smart meter endpoints across the Ikorodu and Ikeja distribution business units.

Thesis Topic

Anomaly Detection in Nigerian Distribution Grid Electricity Theft Using Federated Learning on Smart Meter Data

Transition Signals