## 4 Discussion and Limitations
### Discussion of Specific Topics  
In this section, the focus is on analyzing the main topics where time series and AI have been successfully implemented, presenting also five of the most representative articles from each field. 2.1 Implications of Time Series Forecasting and Artificial Intelligence in the Energy and Electricity Sector  
The energy and electricity topics have become more and more relevant not only for the academic community, but also for stakeholders and governments, since it is crucial now to provide sustainable and affordable sources of energy and power for the population. Using time series and AI, an estimation of production and consumption levels can be achieved. Estimates of solar energy production have been explored by [PERSON] et al. [85], implementing a combination of algorithms, convolutional neural networks (CNN), LSTM, and a transformer, due to the evolution of AI and time series, which now facilitate this approach. The Fingrid open dataset was extracted for training and testing the model, and the outcome demonstrated high efficiency. The results were compared with existing methods, and the accuracy of the hybrid algorithm was higher, positioning it as a new technique that could be integrated into the solar energy domain. Power system safety and stability represent crucial aspects for short-term electricity load time and, according to [PERSON] et al. [PERSON] et al. (2018), electricity demand is marked by high randomness and volatility, with the consumption levels being almost impossible to predict with traditional algorithms. The objective of the authors was to define a machine learning model capable of creating an empirical decomposition that would be divided into subcomponents, and, using a support vector machine and cooperation search algorithm, an estimation of the short-term load data would be performed. A case study has been performed on provincial grid centers in China, showing great results compared to conventional algorithms. The sustainable development of the environment and economy is a key topic that was discussed by [PERSON] et al. [PERSON] et al. (2018), and considers energy consumption a key element. The scope of the research was focused on fuzzy time series and fuzzy c-means clustering for fuzzification and to predict energy consumption using the previous algorithms. The outcome confirms the good stability and accuracy of the algorithms, which are able to predict the structure of energy consumption in an optimal manner. A dynamic transformer for energy power prediction has been developed by [PERSON] et al. [PERSON] et al. (2018), as power estimation is considered to be one of the key elements nowadays for carbon neutrality. Thanks to AI algorithms that have been recently developed, predictions can now be achieved more easily. The authors proposed a dynamic self-differential transformer method (Diformer) that can be used for univariate time series analysis and predictions. The algorithm has been tested on six power time series datasets related to the energy and power domain, showing great results and metrics and outperforming existing models. The prediction of energy consumption using time series data was discussed by [PERSON] et al. [PERSON] et al. (2018), focusing on an analytical evaluation of artificial intelligence, deep learning, and machine learning algorithms that are used for energy consumption estimation and at the same time on implementing the methods in real use cases, extracting datasets related to natural gas, electric power, or energy consumption. The authors underlined the main challenges and potential developments for the research, emphasizing the importance of deep learning and machine learning algorithms in the energy sector. Based on the descriptions provided above for the five most representative papers on energy and electricity areas, the main applicability of time series and AI is focused in the estimation of energy consumption [PERSON] et al. (2018); [PERSON] et al. (2018), energy power prediction [PERSON] et al. (2018), short-term loads [PERSON] et al.