Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment

Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Calavia Domínguez, L.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Sanjuan, J.... (2013). Improved Short-Term Load Forecasting Based on Two-Stage Predictions with Artificial Neural Networks in a Microgrid Environment. Energies. 6(9):4489-4507. doi:10.3390/en6094489S4489450769Brooks, A., Lu, E., Reicher, D., Spirakis, C., & Weihl, B. (2010). Demand Dispatch. IEEE Power and Energy Magazine, 8(3), 20-29. doi:10.1109/mpe.2010.936349Chan, S. C., Tsui, K. M., Wu, H. C., Hou, Y., Wu, Y.-C., & Wu, F. (2012). Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges. IEEE Signal Processing Magazine, 29(5), 68-85. doi:10.1109/msp.2012.2186531Mohan Saini, L., & Kumar Soni, M. (2002). Artificial neural network-based peak load forecasting using conjugate gradient methods. IEEE Transactions on Power Systems, 17(3), 907-912. doi:10.1109/tpwrs.2002.800992Hyndman, R. J., & Fan, S. (2010). Density Forecasting for Long-Term Peak Electricity Demand. IEEE Transactions on Power Systems, 25(2), 1142-1153. doi:10.1109/tpwrs.2009.2036017McSharry, P. E., Bouwman, S., & Bloemhof, G. (2005). Probabilistic Forecasts of the Magnitude and Timing of Peak Electricity Demand. IEEE Transactions on Power Systems, 20(2), 1166-1172. doi:10.1109/tpwrs.2005.846071Amin-Naseri, M. R., & Soroush, A. R. (2008). Combined use of unsupervised and supervised learning for daily peak load forecasting. Energy Conversion and Management, 49(6), 1302-1308. doi:10.1016/j.enconman.2008.01.016Maksimovich, S. M., & Shiljkut, V. M. (2009). The Peak Load Forecasting Afterwards Its Intensive Reduction. IEEE Transactions on Power Delivery, 24(3), 1552-1559. doi:10.1109/tpwrd.2009.2014267Moazzami, M., Khodabakhshian, A., & Hooshmand, R. (2013). A new hybrid day-ahead peak load forecasting method for Iran’s National Grid. Applied Energy, 101, 489-501. doi:10.1016/j.apenergy.2012.06.009Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215Hernandez, L., Baladrón, C., Aguiar, J., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6(3), 1385-1408. doi:10.3390/en6031385Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169Hernández, L., Baladrón, C., Aguiar, J., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Lloret, J. (2013). Experimental Analysis of the Input Variables’ Relevance to Forecast Next Day’s Aggregated Electric Demand Using Neural Networks. Energies, 6(6), 2927-2948. doi:10.3390/en6062927Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.640044

Quantifying Forecast Uncertainty in the Energy Domain

This dissertation focuses on quantifying forecast uncertainties in the energy domain, especially for the electricity and natural gas industry. Accurate forecasts help the energy industry minimize their production costs. However, inaccurate weather forecasts, unusual human behavior, sudden changes in economic conditions, unpredictable availability of renewable sources (wind and solar), etc., represent uncertainties in the energy demand-supply chain. In the current smart grid era, total electricity demand from non-renewable sources influences by the uncertainty of the renewable sources. Thus, quantifying forecast uncertainty has become important to improve the quality of forecasts and decision making. In the natural gas industry, the task of the gas controllers is to guide the hourly natural gas flow in such a way that it remains within a certain daily maximum and minimum flow limits to avoid penalties. Due to inherent uncertainties in the natural gas forecasts, setting such maximum and minimum flow limits a day or more in advance is difficult. Probabilistic forecasts (cumulative distribution functions), which quantify forecast uncertainty, are a useful tool to guide gas controllers to make such tough decisions. Three methods (parametric, semi-parametric, and non-parametric) are presented in this dissertation to generate 168-hour horizon probabilistic forecasts for two real utilities (electricity and natural gas) in the US. Probabilistic forecasting is used as a tool to solve a real-life problem in the natural gas industry. A benchmark was created based on the existing solution, which assumes forecast error is normal. Two new probabilistic forecasting methods are implemented in this work without the normality assumption. There is no single popular evaluation technique available to assess probabilistic forecasts, which is one reason for people’s lack of interest in using probabilistic forecasts. Existing scoring rules are complicated, dataset dependent, and provide less emphasis on reliability (empirical distribution matches with observed distribution) than sharpness (the smallest distance between any two quantiles of a CDF). A graphical way to evaluate probabilistic forecasts along with two new scoring rules are offered in this work. The non-parametric and semi-parametric probabilistic forecasting methods outperformed the benchmark method during unusual days (difficult days to forecast) as well as on other days

Forecasting Natural Gas: A Literature Survey

This work presents a state-of-the-art survey of published papers that forecast natural gas production, consumption or demand, prices and income elasticity, market volatility and hike in prices. New models and techniques that have recently been applied in the field of natural gas forecasting have discussed with highlights on various methodologies, their specifics, data type, data size, data source, results and conclusions. Moreover, we undertook the difficult task of classifying existing models that have been applied in this field by giving their performance for instance. Our objective is to provide a synthesis of published papers in the field of natural gas forecasting, insights on modeling issues to achieve usable results, and the future research directions. This work will help future researchers in the area of forecasting no matter the methodological approach and nature of energy source used. Keywords: Forecasting natural gas; Existing forecasting models; Models categorization. JEL Classifications: C53, Q4, Q4

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

An investigation into visualisation and forecasting of real-time electrical consumption based on smart grid data

The smart grid, and in particular smart meters, is a growing world-wide phenomenon which has allowed for the availability of detailed real time usage data to the user in ways that were not possible in the past. South Africa has been slow-moving in adapting smart meters, but in the past two years this has changed and smart meters are becoming the new standard. This has given rise to the need for software applications to help both the South African consumer and local power utilities get the most out of the smart meter data. The purpose of this research is to investigate the possibilities offered by smart grid data obtained from advanced metering infrastructures, with particular emphasis on real time energy usage visualisation and peak load forecasting. Previously, detailed energy usage data has not been available to consumers hence there has not been much research focusing on utilising this data for direct consumer benefit. The focus of most research has mainly been on the power utilities supply side where attention has been on visualising their consumers’ usage and forecasting consumer demand in order to supply them with electricity continuously and efficiently. In this dissertation a benchmarking model for developing smart grid data visualisation dashboards is proposed and this model is used to present and prototype a consumer side dashboard. The prototype implements real time data visualisation techniques, as well as a Multiple Linear Regression model based forecasting algorithm for half hourly peak load forecasting using data collected from the University of the Witwatersrand’s advanced metering infrastructure. In this study the Multiple Linear Regression model is built through a comprehensive analysis of 2 years’ worth of energy usage data from the University of the Witwatersrand and 3 years’ worth of hourly temperature data from the South African Weather Services. The prototype’s performance is evaluated with reference to the proposed benchmark and a user technology acceptance evaluation done by the University’s Property and Infrastructure Management division. The dashboard is found to be a useful and acceptable tool in energy monitoring at the University. The forecasting model performs well with a mean absolute percentage error of 3.69%. The inclusion of a forecasting functionality within the energy management dashboard is shown to have the ability to help the university reduce its electricity bill by being able to shave their peak loads. The analysis highlights the importance of better data archiving and smart meter monitoring thereby ensuring that the meters are always online and no data goes missing which is vital for accurate forecasting results

Solar Power System Plaing & Design

Photovoltaic (PV) and concentrated solar power (CSP) systems for the conversion of solar energy into electricity are technologically robust, scalable, and geographically dispersed, and they possess enormous potential as sustainable energy sources. Systematic planning and design considering various factors and constraints are necessary for the successful deployment of PV and CSP systems. This book on solar power system planning and design includes 14 publications from esteemed research groups worldwide. The research and review papers in this Special Issue fall within the following broad categories: resource assessments, site evaluations, system design, performance assessments, and feasibility studies

Microgrids

Microgrids are a growing segment of the energy industry, representing a paradigm shift from centralized structures toward more localized, autonomous, dynamic, and bi-directional energy networks, especially in cities and communities. The ability to isolate from the larger grid makes microgrids resilient, while their capability of forming scalable energy clusters permits the delivery of services that make the grid more sustainable and competitive. Through an optimal design and management process, microgrids could also provide efficient, low-cost, clean energy and help to improve the operation and stability of regional energy systems. This book covers these promising and dynamic areas of research and development and gathers contributions on different aspects of microgrids in an aim to impart higher degrees of sustainability and resilience to energy systems