Predicting short-term electricity demand through artificial neural network

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the forecasting error, which makes this topic as an integral part of planning in many companies of various kinds and sizes, ranging from generation, transmission, and distribution to consumption, by requiring reliable forecasting systems

The Data Envelopment Analysis to Determine Efficiency of Latin American Countries for Greenhouse Gases Control in Electric Power Generation

The objective of this research is to determine the efficiency of Latin American countries for the control of GHG emissions due to the generation of electrical energy using the Data Envelopment Analysis (DEA). A positivist epistemic position is assumed, and a methodology of evaluative character is used, comprising five (5) phases. The results show that the countries that are located on the efficient frontier have common police like the increase in the share of renewable energies, and diversification of the energy matrix, which means a better control of GHG emissions. It is possible to determine the efficiency of the public policies established by the countries of Latin America for the control of GHG emissions. In conclusion, the countries that are located on the efficient frontier are those that generate electricity with predominantly renewable sources or, at least, use natural gas as a fuel in greater proportion.  Keywords: Greenhouse Gases (GHG), efficient frontier, Data Envelopment Analysis. JEL Classifications: C1, Q4, Q

Demand in the electricity market: analysis using big data

The traditional business model of energy companies is changing in recent years. The introduction of smart meters has led to an exponential increase in the volume of data available, and their analysis can help find consumption patterns among electric customers to reduce costs and protect the environment. Power plants generate electricity to cover peak consumption at specific times. A set of techniques called “demand response” tries to solve this problem using artificial intelligence proposals. This document proposes a method for processing large volumes of data such as those generated by smart meters. Both for the preprocessing and for the optimization and realization of this analysis big data techniques are used. Specifically, a distributed version of the k-means algorithm and several indices of internal validation of clustering for big data in Spark. The source data correspond to the consumption of electric customers in Bogota, Colombia during the year 2018. The analysis carried out in this study about consumers helps their characterization. This greater knowledge about consumer habits and types of customers can enhance the work of utilities