Abstract
The motion of cloud over a photovoltaic (PV) power station will directly cause the change of solar irradiance, which indirectly affects the prediction of minute-level PV power. Therefore, the calculation of cloud motion speed is very crucial for PV power forecasting. However, due to the influence of complex cloud motion process, it is very difficult to achieve accurate result using a single traditional algorithm. In order to improve the computation accuracy, a pattern classification and particle swarm optimization optimal weights based sky images cloud motion speed calculation method for solar PV power forecasting (PCPOW) is proposed. The method consists of two parts. First, we use a k-means clustering method and texture features based on a gray-level co-occurrence matrix to classify the clouds. Second, for different cloud classes, we build the corresponding combined calculation model to obtain cloud motion speed. Real data recorded at Yunnan Electric Power Research Institute are used for simulation; the results show that the cloud classification and optimal combination model are effective, and the PCPOW can improve the accuracy of displacement calculation.

Abstract
Nowadays, the power quality has become not only an important competitive factor for industrial users but also a crucial factor in the energy efficiency of the facilities. Seen paradoxically, the increase we have seen in the energy efficiency of the electrical loads, leads to the intensive use of power electronics resulting in a very high injection of harmonics in the distribution networks, thus causing a lack in the power quality leading a significant voltage waveform deformation. An electrical network containing a high harmonic content is synonymous of a low-level quality of the distributed energy, resulting in an increase of losses and low power factors, decreasing the energy efficiency of the installations. This paper presents a model that allows predicting the harmonic content present in an electrical installation, depending on the type of loads. Hence, it will be possible to know several qualitative parameters such as the voltage and current total harmonic distortion (THD), which will help to predict and size the mitigation measures to improve the power quality and energy efficiency of the facilities. © 2019 IEEE.

Abstract
Chord progressions play an important role in Western tonal music. For a novice composer, the creation of chord progressions can be challenging because it involves many subjective factors, such as the musical context, personal preference and aesthetic choices. This work proposes ChordAIS, an interactive system that assists the user in generating chord progressions by iteratively adding new chords. At each iteration a search for the next candidate chord is performed in the Tonal Interval Space (TIS), where distances capture perceptual features of pitch configurations on different levels, such as musical notes, chords, and scales. We use an artificial immune system (AIS) called opt-aiNet to search for candidate chords by optimizing an objective function that encodes desirable musical properties of chord progressions as distances in the TIS. Opt-aiNet is capable of finding multiple optima of multi-modal functions simultaneously, resulting in multiple good-quality candidate chords which can be added to the progression by the user. To validate ChordAIS, we performed different experiments and a listening test to evaluate the perceptual quality of the candidate chords proposed by ChordAIS. Most listeners rated the chords proposed by ChordAIS as better candidates for progressions than the chords discarded by ChordAIS. Then, we compared ChordAIS with two similar systems, ConChord and ChordGA, which uses a standard GA instead of opt-aiNet. A user test showed that ChordAIS was preferred over ChordGA and Conchord. According to the results, ChordAlS was deemed capable of assisting the users in the generation of tonal chord progressions by proposing good-quality candidates in all the keys tested.

Abstract
In multi-label classification problems an example can be simultaneously classified into more than one class. This is also a challenging task in Data Streams (DS) classification, where unbounded and non-stationary distributed multi-label data contain multiple concepts that drift at different rates and patterns. In addition, the true labels of the examples may never become available and updating classification models in a supervised fashion is unfeasible. In this paper, we propose a Multi-Label Stream Classification (MLSC) method applying a Novelty Detection (ND) procedure task to update the classification model detecting any new patterns in the examples, which differ in some aspects from observed patterns, in an unsupervised fashion without any external feedback. Although ND is suitable for multi-class stream classification, it is still a not well-investigated task for multi-label problems. We improve a initial work proposed in [1] and extended it with a new Pruned Sets (PS) transformation strategy. The experiments showed that our method presents competitive performances over data sets with different concept drifts, and outperform, in some aspects, the baseline methods.

Abstract

Abstract
A key challenge in the power and energy field is the development of decision-support systems that enable studying big problems as a whole. The interoperability between multi-agent systems that address specific parts of the global problem is essential. Ontologies ease the interoperability between heterogeneous systems providing semantic meaning to the information exchanged between the various parties. The use of ontologies within Smart Grids has been proposed based on the Common Information Model, which defines a common vocabulary describing the basic components used in electricity transportation and distribution. However, these ontologies are focused on utilities' needs. The development of ontologies that allow the representation of diverse knowledge sources is essential, aiming at supporting the interaction between entities of different natures, facilitating the interoperability between these systems. This paper proposes a set of ontologies to enable the interoperability between different types of agent-based simulators, namely regarding electricity markets, the smart grid, and residential energy management. A case study based on real data shows the advantages of the proposed approach in enabling comprehensive power system simulation studies.