Electricity Consumption and Generation Forecasting with Artificial Neural Networks

Nowadays, smart meters, sensors and advanced electricity tariff mechanisms such as time-of-use tariff (ToUT), critical peak pricing tariff and real time tariff enable the electricity consumption optimization for residential consumers. Therefore, consumers will play an active role by shifting their peak consumption and change dynamically their behavior by scheduling home appliances, invest in small generation or storage devices (such as small wind turbines, photovoltaic (PV) panels and electrical vehicles). Thus, the current load profile curves for household consumers will become obsolete and electricity suppliers will require dynamical load profiles calculation and new advanced methods for consumption forecast. In this chapter, we aim to present some developments of artificial neural networks for energy demand side management system that determines consumers’ profiles and patterns, consumption forecasting and also small generation estimations

Key Technical Performance Indicators for Power Plants

In this chapter, we will underline the importance of the key performance indicators (KPIs) computation for power plants’ management. The main scope of the KPIs is to continuously monitor and improve the business and technological processes. Such indicators show the efficiency of a process or a system in relation with norms, targets or plans. They usually provide investors and stakeholders a better image regarding location, equipment technology, layout and design, solar and wind exposure in case of renewable energy sources and maintenance strategies. We will present the most important KPIs such as energy performance index, compensated performance ratio, power performance index, yield, and performance, and we will compare these KPIs in terms of relevance and propose a set of new KPIs relevant for maintenance activities. We will also present a case study of a business intelligence (BI) dashboard developed for renewable power plant operation in order to analyze the KPIs. The BI solution contains a data level for data management, an analytical model with KPI framework and forecasting methods based on artificial neural networks (ANN) for estimating the generated energy from renewable energy sources and an interactive dashboard for advanced analytics and decision support

Decision Support System in National Power Companies. A Practical Example (Part I)

The paper presents the developing stages of the decision support prototype in which the data warehouse and the presentation level are built and validated. The paper also extends the results published in the 12th international conference on Informatics in Economy (IE 2013) proceedings and will presents the major steps for developing the data warehouse that integrates the sources from the Wind Power Plants (WPP) from the national parks and also the interface modules that allow managers to analyze data at a central level

Informatics solution for Smart Garden based on Sensors

This paper aims to develop an application dedicated to the growth and maintenance of plants, whether leguminous or decorative plants, which involves the traditional cultivation method that uses the soil. It consists in two main components: a mobile application that is be able to run on the Android operating system, and the physical component, which is referred to, in what follows, by the term "SmartGarden"

Decision Support System Design for Photovoltaic Systems Operation and Maintenance by using Big Data Technologies

Main goal of this paper is to present a decision support system (DSS) for operation andmaintenance (O&M) of photovoltaic power (PV) systems which are integrated with batterysystems. Such DSS essentially necessitates inclusion of Big Data analytics that will be utilized tomaximize profit from power generation and consumption in a PV-battery integrated system

Data Management for Photovoltaic Power Plants Operation and Maintenance

Main goal of this paper is to present our approach in terms of data management for operationand maintenance (O&M) activities of photovoltaic power (PV) systems that aims to decrease O&Mcosts and increase the availability and life cycle of the PV and profit of the business. The O&M activities targets PV output maximization and cost minimization, improvingperformance and lifetime of the PV. Data management for PV O&M activities, proposed in thispaper, contributes to the long-term performance and revenue capacity of PV

Web Scraping and Review Analytics. Extracting Insights from Commercial Data

Web scraping has numerous applications. It can be used complementary with APIs to extract useful data from web pages. For instance, commercial data is abundant, but not always relevant as it is presented on websites. In this paper, we propose the usage of web scraping techniques (namely, two popular libraries – BeautifulSoup and Selenium) to extract data from web and other Python libraries and techniques (vaderSentiment, SentimentIntensityAnalyzer, nltk, n consecutive words) to analyze the reviews and obtain useful insights from this data. A web scraper is built in which prices are extracted and variations are tracked. Furthermore, the reviews are extracted and analyzed in order to identify the relevant opinions, including complaints of the customers

Electricity Price Evolution and the Disruptive Economic and Geopolitical Context on the Spot Market. A Romanian Case Study

The current context of the electricity markets is marked by the lingering effects of COVID-19 and the conflict in Ukraine that have a significant influence on the European wholesale electricity markets. Both Day-Ahead Market (DAM) and balancing market have been heavily impacted by fluctuating prices. This trend started in October 2021 when the lockdowns were removed and the high request for commodities led to a higher inflation. Then in 2022, the conflict in Ukraine accentuated this evolution and even higher prices were recorded for electricity, gas, oil and other resources. In this paper, we analyze a set of fundamental variables and provide an electricity price forecast on DAM using a multiple regression model. The exogenous variables considered in this paper are the following: power system data (total consumption, total generation and its breakdown: renewables (RES) and Non-RES), economic data (inflation, interest rate), certificate price for CO2 emissions (EU-ETS), level of Danube River and other resources prices (oil, gas). Interesting insights can be extracted from a data set that consists of merged time series collected from January 2019 until August 2022. The results are measured using Mean Absolute Percentage Error (MAPE)