In this paper we introduce a new decentralized digital currency, called NRGcoin. Prosumers in the smart grid trade locally produced renewable energy using NRGcoins, the value of which is determined on an open currency exchange market. Similar to Bitcoins, this currency offers numerous advantages over fiat currency, but unlike Bitcoins it is generated by injecting energy into the grid, rather than spending energy on computational power. In addition, we propose a novel trading paradigm for buying and selling green energy in the smart grid. Our mechanism achieves demand response by providing incentives to prosumers to balance their production and consumption out of their own self-interest. We study the advantages of our proposed currency over traditional money and environmental instruments, and explore its benefits for all parties in the smart grid.

Worldwide scientific community is currently doing a great effort of research in the area of Smart Grids because energy production, distribution, and consumption play a critical role in the sustainability of the planet. The main challenge lies in intelligently integrating the actions of all users connected to the grid. In this context, electricity load forecasting methodologies is a key component for demand-side management. In this research the accuracy of different Machine Learning methodologies is determined for the hourly energy forecasting in buildings. The main goal of this work is to demonstrate the performance of these models and their scalability for different consumption profiles. We propose a hybrid methodology that combines feature selection based on entropies with soft computing and machine learning approaches, i.e. Fuzzy Inductive Reasoning, Random Forest and Neural Networks. They are compared with the traditional statistical time's series forecasting technique ARIMA in order to justify the capability of hybrid methods. In addition, in contrast to the general approaches where offline modelling takes considerable time, the approaches discussed in this work generate fast and reliable models, with low computational costs. These approaches could be embedded, for instance, in a second generation of smart meters, where they could generate onsite electricity forecasting of the next hours, or even trade the excess of energy with other smart meters.

Dealing with missing data is of great practical and theoretical interest in forecasting applications. In this study, we deal with the problem of forecasting with missing data in smart grid and smart home applications, where the information from home area sensors and/or smart meters is sometimes missing, which may hinder or even prevent the forecasting of the next hours and days. In concrete, we focus in a Soft Computing technique called Fuzzy Inductive Reasoning (FIR) and its improved version that can cope with missing information in the forecasting process: flexible FIR. In this article eight different strategies for flexible FIR forecasting are defined and studied taking into account: causal relevance of input variables, consistency of predictions, inertia criterion and K-Nearest Neighbours. Furthermore, we evaluate the implications of prediction accuracy and number of registers predicted, when the number of Missing Values (MVs) in the training dataset is increased progressively. To this end, a real smart grid forecasting application, i.e. electricity load forecasting, has been chosen in this study. The results show that all eight strategies proposed are able to cope with MVs and take advantage of the inherent information in the data, with better results in those strategies making use of causal relevance. In addition, the robustness of flexible FIR and its eight strategies are proved taking into account that the percentage of registers predicted is on average 96.15% when the %MVs in training dataset was around 73%.

Traditional support policies for green energy have greatly contributed to the rise in prosumer numbers.However, it is believed that they will soon start to exert negative impact on stakeholders and on the grid. Policy makers advise to phase out two of the most widely applied policies -net metering and feed-in tariff, in favor of support policies that scale better with rising renewable generation. This work quantifies the impact of these traditional policies in future "what-if" scenarios and confirms the need for their replacement. Based on simulations with real data, we compare net metering and feed-in tariff to four state-of-the-art marketbased mechanisms, which involve auction, negotiation and bitcoin-like currency. The paper examines the extent to which each of these mechanisms motivates not only green energy production but also its consumption. The properties and characteristics of the above mechanisms are evaluated from the perspective of key stakeholders in the low voltage grid -prosumers, consumers and energy providers. The outcome of this study sheds light on current and future issues that are relevant for policy makers in the evolving landscape of the smart grid.

Load forecasting in buildings and homes has been in the last years a task of increasing importance. New services and functionalities can be offered in the home environment due to this predictions, for instance, the detection of potential demand response programs and peaks that may increase the energy bill in a dynamic tariff framework. Almost real-time predictions are key for these services but missing values can dramatically affect the performance of the energy forecasting or distort the prediction significantly. Fuzzy Inductive Reasoning has been proof to model load consumptions with high accuracy compared to other typical AI and statistical techniques. Nevertheless, it has several limitations when missing data is presented in the training data of the model and during the prediction. In this paper, we present an improved version of the Fuzzy Inductive Reasoning, called Flexible FIR Prediction that can cope with missing information in the input pattern as well as, in situations of not found patterns in the behavioural matrix. The new technique has been tested with real data from one building of the Universitat Politècnica de Catalunya (UPC) and the results show that Flexible FIR Prediction is able to generate good predictions with low errors although missing data is present in the training and online prediction phases.