## 5 Experiments and Results  
In this section, the experiments and results will be described. The experiments were carried out on the predicted data. The predictions were about household consumption and power production. The predictions of the consumption were performed using the data of the \"UK Dale\" dataset [20] and were carried out using the aggregated data of the household appliances. The aggregation was performed with the sum of the hourly consumption of each appliance. Three types of users were tested, as described in Section 3.2.1, which differ in age, the number of components in the user nuclei, and the number of appliances. In the following figures, there will be the daily consumption before and after the application of the optimization, with and without the use of meteorological information. Figures 2-4 show the optimized household appliance scheduling. The \"blue\" line is about the meteorological method, meanwhile, the \"green\" line is about the statistical method. The \"red\" line is about the old scheduling. Thus, the graphs compare the old energy consumption to the optimized energy consumption (both hourly based) using the statistical method and the meteorological ones. It can be seen from Figures 2-4 that the meteorological method tends to model appliance utilization similarly to normal utilization, in other words, without optimization. This behavior confirms the assumption that \"time of use is influenced by season\", as written in Section 3.2.1. The scheduling of appliance usage turns out to be adapted to the habits of the households, including in the optimization of the prediction of electricity production. Figure 4: The optimized household appliance scheduling for the third user. Figure 3: The optimized household appliance scheduling for the second user. Figure 2: The optimized household appliance scheduling for the first user. In Table 1, it can be seen that the use of weather information during _GHI_ prediction and consequently power production prediction helps to find an optimal schedule that decreases the daily consumption of the household appliance and consequently the daily cost. These results are related to the fact that the meteorological data gives information that impacts on the quantity of _GHI_ that will be present on the next day of the prediction. So, the power production prediction is more reliable and closer to reality. Meanwhile, the use of statistical information to predict power production is based on information that comes from the past and does not take into account the variation in weather status. Indeed, the savings percentage is increased. This can be seen from the values reported in Table 2. The savings percentage is calculated by subtracting the optimized daily consumption from the initial daily consumption. Then, the value indicating the amount of savings is divided by the initial daily consumption to obtain the savings percentage. This is done for both weather-informed and non-weather-informed optimized consumption. In addition, the percentage of consumption savings depends on the usage habits of household appliances. In Table 2, it can be seen that the meteorological method is better than the statistical method by about 10 percent. In Tables 3 and 4, there is a comparison of the peak consumption and the scheduled usage of the household appliance. Specifically, in Table 3, it can be seen that the peak comparison with the meteorological method; in Table 4, there is the peak comparison with the statistical method. In Table 3, the column \"Difference [%]\" is obtained by the ratio between the difference between the max daily peak not optimized and the optimized one and the max daily peak not optimized. In this way, the percentage of saving consumption is calculated. The same is done in Table 4.  
It is noticeable that the saving percentage for the meteorological method in Table 3 is equal to or higher than zero. This means that, with the meteorological information, the optimization tends to lower the maximum peak of consumption.