Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved.Hernandez, L.; Baladron, C.; Aguiar, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. (2014). Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy. Energy. 75:252-264. doi:10.1016/j.energy.2014.07.065S2522647

Smart Water: Short-Term Forecasting Application in Water Utilities

The unyielding interconnection between water and energy has made demand forecasting a necessity for water utilities. Electricity prices driven by the time of use has impelled water utilities towards short-term water demand forecasting. The progressive new Smart Water Grid platform has helped water utilities in utilizing their Water Distribution Networks. This two-way platform has provided developers and decision makers with robust models that rely on consumer feedback. Among these models is the water demand forecasting models. Multitudinous demand forecasting methods have been developed but none have utilized model implementation practicality. Utilities differ in size, capacity, and interest. While small size utilities focus on model simplicity, larger utilities prioritize model accuracy. This work focuses on a water utility located in Essex County, Ontario, Canada. This study presents three papers that focus on investigation and evaluation of short-term water demand forecasting techniques. The first paper compares water usage between two crops (tomatoes and bell peppers) in an effort to evaluate a crop to crop forecast technique that relies on one crops watering data in order to produce forecasts for another crop, The second paper examines the effect of model type, input type, and data size on model performance and computational load. The third paper proposes a new methodology where model performance is not sacrificed for model simplification

A neural network based technique for short-term forecasting of anomalous load periods

The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to ''anomalous'' load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen's Self Organizing Map (SOM) The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a backpropagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations

Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

On the learning of vague languages for syntactic pattern recognition

The method of the learning of vague languages which represent distorted/ambiguous patterns is proposed in the paper. The goal of the method is to infer the quasi-context-sensitive string grammar which is used in our model as the generator of patterns. The method is an important component of the multi-derivational model of the parsing of vague languages used for syntactic pattern recognition

Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns.This work was supported by European Union Sixth Frame work Program under Project EU-DEEP SES6-CT-2003-503516.Paper no.TPWRS-00633-200

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future