## 6 Conclusions  
With the growing electricity demand, the power system in Nepal is moving from being supply-based to demand-based, where the stability of the system depends on the system's capacity to handle variations in load demand. Being a developing country, the power system has not tackled issues related to demand variation, and high power fluctuation due to variations in demand could lead to power system issues, thus leading to system instability due to unsolved unit commitment problems and resulting in voltage fluctuation, frequency fluctuation brownout, or even blackouts. As a sudden spike in electric load  
Figure 11: Training performance graphs for (**a**) weekdays (**b**) weekends. Figure 12: Actual vs. predicted forecasts achieved by the ANN models with (**a**) a double-layer 16 \(\times\) 16 node structure and (**b**) a single-layer 8 node structure. requires a quick response and high storage capacity in the power system, developed countries rely on forecasting electric load consumption to track the variation in load and identify the unit commitment problem that the system might face in the future. Utilities develop forecasting models that are a day ahead, a week ahead, or a month ahead to allow them to plan generation and, in many cases, implement demand management strategies such as the timed of use tariffs, incentive-based demand response, or even direct control of load to maintain stability in the system. With technological advances on the demand side, policies and strategies based on forecasted load such as critical pricing can help reduce peak load consumption. As barely any research has been performed on load or on electric load consumption in Nepal, this presented study allows the utility to understand the characteristics of the load and its variation due to variables that affect the electric demand of the urban cities of Nepal. This paper presented load forecasting models based on the relation of load with lag loads and temperature for the urban cities of Nepal. Based on the performed correlation study, high correlation with previous lag loads showed that the conventional time series forecasting method could achieve satisfying results for normal working days, whereas the model developed lacked accuracy on weekends such that correlation tended to decrease when moving further back in time, showing limited lag, which could be considered to maintain its accuracy by using the time series model. An improvement in accuracy for load forecasting was achieved by using an FF-ANN through the thorough analysis and selection of input variables. By analyzing the variables affecting the load, it was observed that the variation of electricity demand with the temperature showed both positive as well as negative effects on the demand. For the first six months of the year, the temperature had a positive impact on the demand, which indicates that the use of cooling appliances rose during that period since the mean temperature reached 25 \({}^{\circ}\)C, increasing demand to 1.4 MW in the summer. Similarly, in the next six months, the average temperature decreased to 2.5 \({}^{\circ}\)C, which spiked the demand to 1.7 MW in the winter season. The sensitivity of electricity demand to the temperature was larger in the winter than in the summer, as the rate of demand variation per degree of temperature was observed to be 0.07 MW/\({}^{\circ}\)C during the cold degree days and 0.02 MW/\({}^{\circ}\)C during the hot degree days. The daily load demand and weekly variation showed the daily load activity pattern and type of user. As demand increased from 6:00 a.m., the average demand stayed constant throughout the day until 5:00 p.m. Then, the demand stayed high from 6:00 p.m. to 9:00 p.m. This study indicated the variation of load on weekdays and weekends such that a single model was not able to analyze both cases; a different model was required to forecast the demand on the weekends.