Renewable energy is defined as the contribution of renewables to total primary energy supply (TPES). Renewables include the primary energy equivalent of hydro (excluding pumped storage), geothermal, solar, wind, tide and wave sources. Energy derived from solid biofuels, biogasoline, biodiesels, other liquid biofuels, biogases and the renewable fraction of municipal waste are also included. Biofuels are defined as fuels derived directly or indirectly from biomass (material obtained from living or recently living organisms). This includes wood, vegetal waste (including wood waste and crops used for energy production), ethanol, animal materials/wastes and sulphite lyes. Municipal waste comprises wastes produced by the residential, commercial and public service sectors that are collected by local authorities for disposal in a central location for the production of heat and/or power. This indicator is measured in thousand toe (tonne of oil equivalent) as well as in percentage of total primary energy supply.

This dataset contains stock market data for prominent energy companies operating within the Energy Infrastructure and Transportation industry, namely EPD, PAA and TRP. It encompasses 5 years of trading data, including daily opening and closing prices, the highest and lowest prices recorded during the day, and the trading volume for each respective day. The dataset serves as a valuable resource for investors, analysts, and researchers to examine and analyze the performance of major energy companies in the sector. The stock price movements of energy companies provide crucial insights to market participants, helping them gain an understanding of past performance and price trends. As the dataset includes daily price changes for traded shares, it becomes a valuable tool for those developing short-term or long-term investment strategies. Additionally, it can be utilized in financial analysis processes, such as risk management and portfolio diversification. Analysts can evaluate the performance of energy companies by studying the differences between opening and closing prices, highest and lowest values, trading volumes, and other technical analysis tools. Furthermore, this dataset facilitates inter-company comparisons and enables monitoring of sector-wide trends within the energy industry. In summary, this dataset is a valuable and informative resource for individuals seeking to analyze and evaluate the stock performance of significant energy companies operating in the sector. Investors and financial professionals can use this data to make informed decisions, minimize risks, and assess potential opportunities.

This dataset contains stock market data for prominent energy companies operating within the Refinery and Distribution, namely MPC, PSX and VLO. It encompasses 5 years of trading data, including daily opening and closing prices, the highest and lowest prices recorded during the day, and the trading volume for each respective day. This dataset, which provides the stock performance analysis of major companies in the Refinery and Distribution sector such as MPC, PSX, and VLO, holds significant value for investors, analysts, and researchers. Due to its inclusion of daily stock price changes, this dataset is a crucial tool for those evaluating both short-term and long-term investment strategies for these companies. Analysts interested in assessing the performance of leading companies in the Refinery and Distribution sector, including MPC, PSX, and VLO, can actively utilize this comprehensive dataset. Encompassing data on the differences between opening and closing prices, highest and lowest values, trading volume, and other technical analysis tools, this dataset provides essential information for understanding sector trends and making company comparisons. Investors and financial experts seeking to examine and evaluate the stock performance of MPC, PSX, and VLO in the Refinery and Distribution sector will greatly benefit from this valuable dataset. Through this data, investors can make more informed decisions, reduce risks, and discover potential opportunities. This comprehensive dataset serves as a reliable and detailed guide for future investment strategies, offering essential insights into the stock performance of these significant companies.

This agent leverages large language model capabilities to automatically detect entities and relationships in texts, create or update ontology schemas, and integrate with existing knowledge graphs by adding new data and relationships. As a result, it provides a richer, more consistent, and semantically structured knowledge graph infrastructure for corporate or research purposes. This enhanced knowledge graph supports AI solutions in delivering more accurate and context-aware responses.