Abstract
Torrefaction of Biomass for Energy Applications: From Fundamentals to Industrial Scale explores the processes, technology, end-use, and economics involved in torrefaction at the industrial scale for heat and power generation. Its authors combine their industry experience with their academic expertise to provide a thorough overview of the topic. Starting at feedstock pretreatment, followed by torrefaction processes, the book includes plant design and operation, safety aspects, and case studies focusing on the needs and challenges of the industrial scale. Commercially available technologies are examined and compared, and their economical evaluation and life cycle assessment are covered as well. Attention is also given to non-woody feedstock, alternative applications, derived fuels, recent advances, and expected future developments. For its practical approach, this book is ideal for professionals in the biomass industry, including those in heat and power generation. It is also a useful reference for researchers and graduate students in the area of biomass and biofuels, and for decision makers, policy makers, and analysts in the energy field. Compares efficiency and performance of different commercially available technologies from the practical aspects of daily operation in an industrial scale plant. Presents a cost analysis of the production, logistics, and storage of torrefied biomass. Includes case studies addressing challenges that may occur in the daily operation in an industrial scale plant. Covers other associated technologies, the densification of torrefied biomass, and non-woody feedstock.

Abstract
Spreadsheets are used in professional business contexts to make decisions based on collected data. Usually, these spreadsheets are developed by end users (e. g. accountants) in an ad-hoc way. The effect of this practice is that the business logic of a concrete spreadsheet is not explicit to them. Thus, its correctness is hard to assess and users have to trust. We present an approach where structure and computational behavior of a spreadsheet are specified by a model with a process-like notation based on combining pre-defined functional spreadsheet services with typed interfaces. This allows for a consistent construction of a spreadsheet by defining its structure and computational behavior as well as filling it with data and executing the defined computational behavior. Thus, concrete spreadsheets are equipped with a specification of their construction process. This supports their understanding and correct usage, even in case of legacy spreadsheets. The approach has been developed in cooperation with an industrial partner from the automotive industry.

Abstract
This paper proposes a Predictive Dispatch System (PDS) as part of a Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed PDS consists of a Decision-Making System (DMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. A Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Home Energy Management (HEM) problem. Moreover, the proposed method to solve HEM problem is based on the Moving Window Algorithm (MWA). The performance of the proposed Home Energy Management System (HEMS) is evaluated using a JADE implementation of the MASHES.

Abstract
Secondary Reserve Requirements (SRR) are usually estimated based upon unit failure rates, and demand and intermittent productions forecasting errors. These requirements are very often inputs to energy and reserve generation dispatch models. However, for the long term, the fact that renewable generation investments must also be computed, affects these requirements. This paper proposes a new Unit Commitment (UC) to represent the SRR in long-term electricity generation models as a function of the renewable investment decisions. Specifically, SRRs are computed as a function of the forecasting errors of renewable productions, and of the unavailability rates of the generation units, which are also outputs of the UC. The case studies show that, when SRRs are endogenous, investments in renewable generation can be lower than expected due to the additional reserve costs these technologies involve. © 2017 IEEE.

Abstract
Crowdsourcing has become an essential source of information for tourists and the tourism industry. Every day, large volumes of data are exchanged among stakeholders in the form of searches, posts, shares, reviews or ratings. This paper presents a tourist-centred analysis of crowd-sourced hotel information collected from the Expedia platform. The analysis relies on Data Mining methodologies to predict trends and patterns which are relevant to tourists and businesses. First, we propose an approach to reduce the crowd-sourced data dimensionality, using correlation and Multiple Linear Regression to identify the single most representative rating. Finally, we use this rating to model the hotel customers and predict hotel ratings, using the Alternating Least Squares algorithm. In terms of contributions, this work proposes: (i) a new crowd-sourced hotel data set; (ii) a crowd-sourced rating analysis methodology; and (iii) a model for the prediction of personalised hotel ratings.

Abstract
This paper presents the demonstration of an energy resources management approach using a physical smart city model environment. Several factors from the industry, governments and society are creating the demand for smart cities. In this scope, smart grids focus on the intelligent management of energy resources in a way that the use of energy from renewable sources can be maximized, and that the final consumers can feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This work thus presents an innovative means to enable a realistic, physical, experimentation of the impacts of novel energy resource management models, without affecting consumers. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles.