The neural network must be trained; i.e., examples of the problem to be solved must be presented to the network, then connection weights must be adjusted based on the difference between the output obtained and the desired data (ground truth). On the daily average prices, we implemented a ...
In this analysis, grid connection costs and taxes applicable to the energy imported from the grid, and potential feed-in tariff have not been considered. 3. Results and Discussion This section presents the results of the optimization. In Section 3.1, the baseline scenario is presented, which ...
Figure 1 presents a structural overview of the methodology, graphically illustrating the connection between the following Section 2.2, Section 2.3 and Section 2.4 as well as the final output of this exercise, namely the generated conversion factor database and the accompanying results and discussion ...
Thus, it can be concluded that the formation of a common electricity market is going according to plan despite the conditions changing in connection with the imposition of sanctions against Russia. Electricity generation will certainly increase. Of course, the question remains as to its structure and...
Hence, the SA training of the FFANN model will result global optimal values for the FFANN connection weights, which corresponds to a higher electricity demand forecasting accuracy. The SA optimization technique is computationally simple and fast convergent for a given configuration of the FFANN ...
For instance, Icelandic GOs can be traded and used for disclosure in mainland Europe even without a physical grid connection [27]. This means that when consumers purchase green electricity certified by GOs, they may mistakenly believe they are supporting the production of local renewable energy ...