In grid-deficient environments, residential energy systems face severe carbon emission penalties due to mandatory reliance on diesel standby generators during supply interruptions. In Iraq, summer peak loads routinely exceed grid capacity, triggering prolonged generator operation and dramatically increasing household carbon footprints. This study presents a deep Q-network (DQN) reinforcement learning framework for intelligent battery energy storage system (BESS) scheduling, targeting carbon emissions reduction through strategic peak shaving. The DQN agent learns optimal battery dispatch strategies by internalizing diurnal patterns in load and solar generation through temporal state features, enabling anticipatory control without requiring explicit external forecasting models. The system is trained on one-year operational data from a representative Iraqi residential installation and evaluated over the critical summer period (122 days, 35.5% grid unavailability). The results demonstrate a 54.8% ${\mathrm{CO}}_2$ reduction (306.5 kg versus 677.4 kg baseline), a 25.5% reduction in generator runtime, and a 23.7% reduction in operating costs for the studied configuration. The learned policy approaches 89.6% of perfect-foresight MILP performance while executing 35,000 times faster. A reward function sensitivity analysis across five weighting schemes confirms that the 20:1 carbon-to-cost priority ratio optimally balances environmental and economic objectives. Ablation studies quantify the mechanism contributions: anticipatory pre-charging accounts for 58% of the total improvement, discharge optimization for 44%, and real-time PV coordination for 22%. These findings establish DQN-based BESS optimization as a practically deployable decarbonization approach for residential systems in grid-constrained developing regions.

Optimal battery energy storage system (BESS) scheduling in regions withunreliable electricity infrastructure remains critically underexplored despiteaffecting approximately 1.2 billion people globally. This study developsa novel hybrid forecasting framework and evaluates three scheduling algo-rithms for residential BESS operation in Iraq, characterized by severe gridinstability (4-10 hour daily outages), extreme seasonal load variations (400%summer-to-winter differential), and reliance on diesel backup generation. Theforecasting methodology integrates artificial neural network prediction withhigh-frequency data mining (1 kHz sampling) for device-level signature recog-nition, achieving coefficient of determination (R²) improvement from 0.84to 0.94-0.95 and reducing peak load prediction error from 12.7% to 5.2%.Three paradigms were systematically evaluated: Adaptive Threshold-BasedScheduling (ATBS), Mixed Integer Linear Programming (MILP), and ModelPredictive Scheduling (MPS). Multi-site validation across six residences innorthern, central, and southern Iraq over a complete annual cycle revealsalgorithm-specific trade-offs. MILP achieves maximum peak demand reduc-tion (46.0%) with 93.5% renewable self-consumption but requires 8.4 sec-onds computation time and exhibits sensitivity to forecast accuracy. MPSdemonstrates robust performance with 31.2% peak reduction and 96.8% self-consumption through real-time adaptability (0.3 seconds execution). ATBSprovides forecast-independent operation with 8.9% peak reduction and 95.7%self-consumption at negligible computational cost. Full-year techno-economicevaluation across 365 days reveals that MILP achieves 32.9% household gen-erator energy reduction and 1.38 tonnes/year CO2 avoidance, while ATBSprovides 23.5% reduction at lowest computational cost. Under the veri-fied Iraqi generator retail rate of $0.29/kWh (52.2 USD/180 kWh), batterydegradation costs marginally exceed generator fuel savings for all algorithms,yielding negative net present value under current tariff structures. However,CO2 abatement costs of $35-44/tonne fall below social cost of carbon esti-mates, providing environmental justification for deployment. Community-scale analysis (1,000 homes) demonstrates MILP deployment would avoid1,380 tonnes CO2 annually, equivalent to removing 300 passenger vehicles.Full-year techno-economic evaluation reveals that battery degradation costsmarginally exceed generator fuel savings under current Iraqi tariff structures($0.29/kWh), yielding negative net present value for all algorithms under a$7,000 battery system cost; however, CO2 abatement costs of $35-44/tonnefall below social cost of carbon estimates, providing environmental justifi-cation for deployment. Breakeven analysis projects economic viability atbattery costs below $3,500-4,000-approaching feasibility given ongoing bat-tery price declines. Findings establish quantitative frameworks for algorithmselection based on operational priorities, demonstrating a viable near-termpathway for residential energy management in grid-constrained developingregions serving 1.5 billion people globally.

Optimal battery energy storage system (BESS) scheduling in regions withunreliable electricity infrastructure remains critically underexplored despiteaffecting approximately 1.2 billion people globally. This study developsa novel hybrid forecasting framework and evaluates three scheduling algo-rithms for residential BESS operation in Iraq, characterized by severe gridinstability (4-10 hour daily outages), extreme seasonal load variations (400%summer-to-winter differential), and reliance on diesel backup generation. Theforecasting methodology integrates artificial neural network prediction withhigh-frequency data mining (1 kHz sampling) for device-level signature recog-nition, achieving coefficient of determination (R²) improvement from 0.84to 0.94-0.95 and reducing peak load prediction error from 12.7% to 5.2%.Three paradigms were systematically evaluated: Adaptive Threshold-BasedScheduling (ATBS), Mixed Integer Linear Programming (MILP), and ModelPredictive Scheduling (MPS). Multi-site validation across six residences innorthern, central, and southern Iraq over a complete annual cycle revealsalgorithm-specific trade-offs. MILP achieves maximum peak demand reduc-tion (46.0%) with 93.5% renewable self-consumption but requires 8.4 sec-onds computation time and exhibits sensitivity to forecast accuracy. MPSdemonstrates robust performance with 31.2% peak reduction and 96.8% self-consumption through real-time adaptability (0.3 seconds execution). ATBSprovides forecast-independent operation with 8.9% peak reduction and 95.7%self-consumption at negligible computational cost. Full-year techno-economicevaluation across 365 days reveals that MILP achieves 32.9% household gen-erator energy reduction and 1.38 tonnes/year CO2 avoidance, while ATBSprovides 23.5% reduction at lowest computational cost. Under the veri-fied Iraqi generator retail rate of $0.29/kWh (52.2 USD/180 kWh), batterydegradation costs marginally exceed generator fuel savings for all algorithms,yielding negative net present value under current tariff structures. However,CO2 abatement costs of $35-44/tonne fall below social cost of carbon esti-mates, providing environmental justification for deployment. Community-scale analysis (1,000 homes) demonstrates MILP deployment would avoid1,380 tonnes CO2 annually, equivalent to removing 300 passenger vehicles.Full-year techno-economic evaluation reveals that battery degradation costsmarginally exceed generator fuel savings under current Iraqi tariff structures($0.29/kWh), yielding negative net present value for all algorithms under a$7,000 battery system cost; however, CO2 abatement costs of $35-44/tonnefall below social cost of carbon estimates, providing environmental justifi-cation for deployment. Breakeven analysis projects economic viability atbattery costs below $3,500-4,000-approaching feasibility given ongoing bat-tery price declines. Findings establish quantitative frameworks for algorithmselection based on operational priorities, demonstrating a viable near-termpathway for residential energy management in grid-constrained developingregions serving 1.5 billion people globally.

Optimal battery energy storage system (BESS) scheduling in regions withunreliable electricity infrastructure remains critically underexplored despiteaffecting approximately 1.2 billion people globally. This study developsa novel hybrid forecasting framework and evaluates three scheduling algo-rithms for residential BESS operation in Iraq, characterized by severe gridinstability (4-10 hour daily outages), extreme seasonal load variations (400%summer-to-winter differential), and reliance on diesel backup generation. Theforecasting methodology integrates artificial neural network prediction withhigh-frequency data mining (1 kHz sampling) for device-level signature recog-nition, achieving coefficient of determination (R²) improvement from 0.84to 0.94-0.95 and reducing peak load prediction error from 12.7% to 5.2%.Three paradigms were systematically evaluated: Adaptive Threshold-BasedScheduling (ATBS), Mixed Integer Linear Programming (MILP), and ModelPredictive Scheduling (MPS). Multi-site validation across six residences innorthern, central, and southern Iraq over a complete annual cycle revealsalgorithm-specific trade-offs. MILP achieves maximum peak demand reduc-tion (46.0%) with 93.5% renewable self-consumption but requires 8.4 sec-onds computation time and exhibits sensitivity to forecast accuracy. MPSdemonstrates robust performance with 31.2% peak reduction and 96.8% self-consumption through real-time adaptability (0.3 seconds execution). ATBSprovides forecast-independent operation with 8.9% peak reduction and 95.7%self-consumption at negligible computational cost. Full-year techno-economicevaluation across 365 days reveals that MILP achieves 32.9% household gen-erator energy reduction and 1.38 tonnes/year CO2 avoidance, while ATBSprovides 23.5% reduction at lowest computational cost. Under the veri-fied Iraqi generator retail rate of $0.29/kWh (52.2 USD/180 kWh), batterydegradation costs marginally exceed generator fuel savings for all algorithms,yielding negative net present value under current tariff structures. However,CO2 abatement costs of $35-44/tonne fall below social cost of carbon esti-mates, providing environmental justification for deployment. Community-scale analysis (1,000 homes) demonstrates MILP deployment would avoid1,380 tonnes CO2 annually, equivalent to removing 300 passenger vehicles.Full-year techno-economic evaluation reveals that battery degradation costsmarginally exceed generator fuel savings under current Iraqi tariff structures($0.29/kWh), yielding negative net present value for all algorithms under a$7,000 battery system cost; however, CO2 abatement costs of $35-44/tonnefall below social cost of carbon estimates, providing environmental justifi-cation for deployment. Breakeven analysis projects economic viability atbattery costs below $3,500-4,000-approaching feasibility given ongoing bat-tery price declines. Findings establish quantitative frameworks for algorithmselection based on operational priorities, demonstrating a viable near-termpathway for residential energy management in grid-constrained developingregions serving 1.5 billion people globally.

Motor current signature analysis (MCSA) enables non-invasive condition monitoring of three-phase induction motors, but most published studies use short laboratory records with artificially seeded faults and report only a discrete fault label without any measure of confidence. This paper proposes a physics-informed temporal convolutional network (PI-TCN) that is trained and tested on 12 months of stator-current records measured on a 90 kW industrial induction motor in service. Fault labels were assigned retrospectively by aligning the recordings with documented maintenance interventions. From each 10 s current snapshot, 60 features are computed that describe time-domain statistics, inter-phase symmetry, harmonic distortion, the energy at theoretical fault frequencies, and the shape of the spectrum. Using a 24 h history of these features, the network classifies 10 motor conditions, estimates a continuous fault severity between 0 and 1, and provides prediction intervals for the severity. To avoid optimistic results, the data are divided by fault episode, with a temporal gap between training and test data. On the independent test set, the PI-TCN reaches an accuracy of $0.991\pm 0.002$ and a macro-averaged $F_1$ score of $0.898\pm 0.009$ over five training runs, and estimates the severity of faulty windows with a mean absolute error of $0.031\pm 0.004$. A five-member ensemble of the network gives prediction intervals whose empirical coverage matches the nominal level from 50% to 95% (expected calibration error 0.015), whereas split-conformal intervals under-cover at the low levels because calibration and test windows are not exchangeable under the temporal split. A label-perturbation study shows that a one-day error in the fault onset lowers the macro-averaged $F_1$ by up to 0.11 or more than doubles the severity error, depending on its direction. Because the method uses only the existing plant data logger, it can be deployed without additional sensors.

Effective water management requires accurate forecasting of dam inflows, rainfall, and actual electricity demand. In this paper, we propose a framework for intelligent control of Haditha Dam gates using the improved Harris Hawks Multi-Objective algorithm (IMOHHO) with Long Short-Term Memory (LSTM). The study aims to develop a framework for scheduling the operation of the gates that strikes a balance between maximizing hydropower generation, maintaining storage levels close to the target value, reducing unused discharge, and optimizing the operations of the gates. This ensures that a minimum net head of a 147 m water level in the dam reservoir is maintained. The proposed model was applied to the Haditha Dam using operational gates and a 48-step time horizon with an interval of 30 min that could be changed as needed. In the IMOHHO-LSTM algorithm, each filtered solution represents an integrated timeline for the opening of the six gates throughout the runtime; the algorithm relies on the concept of non-dominant solutions and creates an archive of optimal solutions using the Pareto interface to achieve a balance between different operational objectives. The final results showed the potential of the proposed algorithm to ensure the efficient operation of the dam, with a total power generation of $\mathrm{10,434.14}$ MWh, while the average output power was $434.76$ MW. The maximum capacity reached 660 MW, which represents the maximum design capacity of a modern dam. Furthermore, the model kept the storage level within a very narrow range of 2199 to 2206 Mm³, centering around the target storage value of 2200 Mm³. This confirms the passing of the IMOHHO-LSTM algorithm in balancing energy production with maintaining the operational requirements of the dam lake’s water level.