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Demand for electricity is constantly increasing, and production is facing new constraints due to the current world situation. An alternative to standard energy production methodologies is based on the use of renewable sources; however, these methodologies do not produce energy consistently due to weather factors. This results in a significant commitment of the user who must appropriately distribute loads in the most productive time slots. In this paper, a comparison is made between two methods of predicting solar energy production, one statistical and the other meteorological. For this work, a system capable of presenting the scheduling of household appliances is tested. The system is able to predict the energy consumption of the users and the energy production of the solar system. The system is tested using data from three different users, and the mean percentage of consumption reduction is about 77.73%. This is achieved through optimized programming of appliance use that also considers user comfort. [ABSTRACT FROM AUTHOR]

The paper introduces the feasibility of achieving zero energy on University buildings in Egypt through retrofitting three buildings affiliated to Faculty of Engineering, Mansoura University )two of them are existing buildings and the third one is under construction( . The paper used the design Builder software, that was used to calculate energy consumption for each building and then to upgrade them. Also, a simulation tool was used to optimize the electricity consumption of the building that is under construction through getting the optimal cooling system. It is evident from the results that retrofitting decreases energy consumption by 50 -90% with the possibility of achieving zero energy by using renewable energy sources such as solar cells. [ABSTRACT FROM AUTHOR]

In today's world, renewable energy sources are increasingly integrated with nonrenewable energy sources into electric grids and pose new challenges because of their intermittent and variable nature. Energy prediction using soft-computing techniques plays a vital role in addressing these challenges. As electricity consumption is closely linked to other energy sources such as natural gas and oil, forecasting electricity consumption is essential for making national energy policies. In this paper, we utilize various data mining techniques, including preprocessing historical load data and the load time series's characteristics. We analyzed the power consumption trends from renewable energy sources and nonrenewable energy sources and combined them. A novel machine learning-based hybrid approach, combining multilayer perceptron (MLP), support vector regression (SVR), and CatBoost, is proposed in this paper for power forecasting. A thorough comparison is made, taking into account the results obtained using other prediction methods. [ABSTRACT FROM AUTHOR]