## 5 Conclusions  
This paper aimed at presenting a comprehensive demand response model for the Jordanian power sector, based on the maximization of the profit for the service provider, considering its interaction with the power generators and customers. To this aim, a peak period, day-ahead DR based on the deep neural networks model, was introduced, and applied to the residential sector, which holds a large portion of the daily demand. The hourly day-ahead demand prediction, which is one of the main inputs to the DR model, was achieved by training a deep neural network on four-years of demand data showed perfect estimation with a MAPE error of 1.411% on the first test data set and 2.03% on the second one at the end of the four years training period. Besides, a precise PEMD estimation of Jordan's residential sector was based on recent research on the short-term price elasticity of Jordan's residential and the analysis of the different electrical appliances and daily operations. The results of the DR model applied for multiple case scenarios of the PEMD showed that peak reductions, load factor improvements, as well as high potential for significant cost saving could be achieved with the proposed DR model. The proposed model at its current state faces the following limitations that should be taken into consideration for further development. First, due to the unavailability of a detailed electricity dispatching model for the Jordanian power sector, this part was simplified. Therefore, only the average prices and costs of the dispatched power plants were used in this study. By obtaining and including a more general and comprehensive dispatch model, more accurate modeling of the profit and saving calculation can be achieved, and a more precise optimization can be implemented. Second, in the presented model, the hourly residential demand of each distribution company was assumed to be a constant percentage of its share of the peak demand. Third, the hourly renewable energy power is considered given data to the model provided by each renewable power plant. Thus, a comprehensive prediction model should be combined with the current DR model to  
Figure 24: Power plant shares for the Case of C7-300% before and after DR.  
assess the impact of the climate conditions on balancing electricity supply and demand and predicting the demand response. In the presented model, and due to the lack of full data related to weather conditions, these features were not fully integrated aside from the temperature. With that said, both the Government and all key players in the power sector must take action to hasten the development of DR response systems for the different sectors of the Jordanian electricity sector and take into consideration policies to further increase the deployment of residential smart meters. Furthermore, research towards viable DR programs both price and incentive-based that attract the residential and different sectors in Jordan's power sector should be implemented, as well as pushing awareness campaigns to the importance of DR systems and mature energy usage to achieving a smarter grid that can handle the increasing challenges faced by high demands and high penetration of renewable energies as well as achieve environmental goals. In addition, to hasten the development of a smarter Jordanian grid, where DR systems are a part of, the government should provide more incentive to enable researchers and related personals by implementing policies that can facilitate easier access to available data, expertise and connect said parties further to accelerate development and research in these promising areas. For future work, the model will further be extended to consider periods outside of the peak period, including forecasting models for solar and wind, using a more detailed power dispatch model, and testing different DR models for the Jordanian power grid. Conceptualization and methodology: A.S. and H.F.; Prediction modelling: A.S., H.F. and H.A.