Some remarks on exp-function method and its applications

Recently, many important nonlinear partial differential equations arising in the applied physical and mathematical sciences have been tackled by a popular approach, the so-called Exp-function method. In this paper, we present some shortcomings of this method by analyzing the results of recently published papers. We also discuss the possible improvement of the effectiveness of the method

New Solitary Wave Solutions in Higher-Order Wave Equations of the Korteweg -de Vries Type

In this work we study two partial differential equations that constitute second-and third-order approximations of water wave equations of the Korteweg -de Vries type. In particular, we first study previous results concerning the derivation of solitary wave solutions of the second-order approximation. We then use a simple assumption and find new solitary wave solutions for both equations

The exp-function method and n-soliton solutions

We generalize the exp-function method recently proposed by He and Wu [Chaos, Solitons and Fractals 30, 700 (2006)]. We apply this generalized method to the Korteweg-de Vries equation and derive the known 2-soliton and 3-soliton solutions. We also discuss the efficiency, as well as the drawbacks of the proposed method

A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm,leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10\% of the dataset, exhibit significant internal dissimilarity and thus it splits them even further to create nine clusters in total. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted Demand Response programs.Comment: 29 pages, 19 figure

Big Data for Energy Management and Energy-Efficient Buildings

European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings

An Advanced IoT-based System for Intelligent Energy Management in Buildings

The energy sector is closely interconnected with the building sector and integrated Information and Communication Technologies (ICT) solutions for effective energy management supporting decision-making at building, district and city level are key fundamental elements for making a city Smart. The available systems are designed and intended exclusively for a predefined number of cases and systems without allowing for expansion and interoperability with other applications that is partially due to the lack of semantics. This paper presents an advanced Internet of Things (IoT) based system for intelligent energy management in buildings. A semantic framework is introduced aiming at the unified and standardised modelling of the entities that constitute the building environment. Suitable rules are formed, aiming at the intelligent energy management and the general modus operandi of Smart Building. In this context, an IoT-based system was implemented, which enhances the interactivity of the buildings’ energy management systems. The results from its pilot application are presented and discussed. The proposed system extends existing approaches and integrates cross-domain data, such as the building’s data (e.g., energy management systems), energy production, energy prices, weather data and end-users’ behaviour, in order to produce daily and weekly action plans for the energy end-users with actionable personalised information

Local communities towards a sustainable energy future: needs and priorities

The local authorities demonstrate their willingness to implement sound local sustainable energy policies, especially through their participation in the Covenant of Mayors (CoM). However, in rural environments, namely areas outside of large cities and towns, fulfilling their CoM commitments, especially as regards the local energy planning at the medium- to long-term scale, can come with very different and sometimes challenging constraints. In this context, the main objective of this paper is the assessment of the local communities’ needs and priorities, so as to identify the key parameters that should be taken into consideration during the development of their Sustainable Energy Action Plan. The adopted approach was implemented in rural communities from four countries (Austria, Croatia, Greece and Portugal). From the results obtained, the need for a methodology, appropriately customised to the rural communities’ characteristics, was determined, addressing especially interested stakeholders who are not ‘experts’ in the field