Intelligent Energy Scheduling for distributed Duhok Polytechnic University: A Chimpanzee Optimization Approach for Load Reduction - Hayveen Saleem Sadiq; Mohammed A.M. Sadeeq (Statistics, Optimization & Information Computing, 2025)
Universities operate some of the most energy-intensive real-estate on any public grid, yet their predictable timetables and centralized governance make them ideal testbeds for advanced demand-side control. This paper presents an Intelligent Energy Scheduling (IES) platform deployed across two geographically separated laboratories of Duhok Polytechnic University (DPU-ALDOSKI). Thirty-second electrical measurements are streamed to a nightly pipeline that (i) cleans and aggregates the data into hourly features, (ii) issues 24-step load forecasts with a seasonal ARIMA model, and (iii) converts academic timetables, public holidays and device-specific weekends into a binary permission mask. The resulting day-ahead scheduling task is solved by a binary Chimpanzee Optimization Algorithm (ChOA) that manipulates a 48-gene on/off chromosome while penalizing unmet demand and excessive switching  .Over an eleven-month evaluation horizon (13 Feb 2022 – 19 Jan 2023), the scheduler reduced Total Consumed Power by 24 %, avoiding 80 MWh of electricity and 46 t CO₂-e without any hardware retrofits. Paired t-tests and Wilcoxon–Mann–Whitney tests returned p < 10⁻⁴ for every monitored variable, confirming statistical significance, while the optimizer’s runtime averaged ≈ 180 ms per device per day, well within the one-minute nightly budget. An open-source repository containing anonymized data, configuration files and turnkey code accompanies the paper, providing a reproducible benchmark for future campus-scale demand-response research and demonstrating the practical viability of ChOA-driven scheduling in live institutional setting