The European Union targets aim to replace the non-renewable energy sources (non-RES) of coal, oil and gas (COG) generation with RES and storage (RES-S). The replacement of COG-generating units will lead to a decrease in CO2 emissions and a better living environment. Starting from this desideratum, in this paper, we create several scenarios to replace COG in Romania with RES-S, reconsider future energy mixes and engage with a more creative planning in order to meet the clean energy transition path. The energy shortages, especially in European countries after the Russian invasion of Ukraine, led many governments (including the Romanian, Polish, etc.) to think more about short-term supply issues and less about medium- and long-term power system planning. However, the decision makers of the European power systems have to decide how fast to avoid firing coal, how fast to adopt RES and how fast to invest in flexibility sources, including storage stations to enable a higher integration of RES. Therefore, in this paper, a holistic view to envision the RES and non-RES contribution to the load coverage in Romania for a smooth transition to a low-carbon economy is provided. The results show that an initial mix of wind, photovoltaic (PV) and storage systems is preferable to substitute 600 MW of installed power in coal-based power plants. Furthermore, the case of Poland—the European country with over 70% coal in its generation portfolio—is also presented as it can serve as a good example.
Smart meters allow electricity consumption readings at a high time resolution generating time series that can be investigated to extract valuable insights and detect frauds. Using a dataset with recordings from Chinese consumers, we propose an exploratory data analysis and processing to train several classifiers and assess the results. Good results are obtained with ensemble classifiers such as Random Forest (RF), eXtreme Gradient Boosting (XGB) and Multi-Layer Perceptron (MLP) with two layers and a relatively small number of neurons. Real-consumption dataset daily recorded in China consisting of over 42,000 consumers and over 1,000 days is processed with machine learning ML algorithms or classifiers to distinguish between normal and suspicious consumers. In this paper, we will compare a simple feature engineering method that consists in aggregating the data, calculating distances and density function with no feature engineering, proving that the first approach enhances the results and reduces the utility companies’ costs related to on-site inspections. The results are compared with AUC score and ROC curves as the input data is highly skewed.
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