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It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. Backtracking search algorithm as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information and support vector machine. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

Technology advancement in the last few decades allows large penetration of renewable energy resources in the distribution network (DN). The integration of such resources has shown a substantial impact on DN through power loss reduction and improved network reliability. Besides this, the existing protection system has encountered coordination challenges due to the bidirectional power flow, different types and capacity of generation sources, and changes in fault levels due to network operating modes (grid-connected or islanded). Such conditions may cause the relays to malfunction and imperil the effectiveness of the existing protection scheme. Therefore, an efficient and robust protection coordination scheme is imperative to avoid network reliability and stability issues to the grid. This review paper presents a comparative analysis of various protection techniques implemented to alleviate the impact of integrated resources into DN. Moreover, a comparison of classical and modified protection approaches in terms of advantages, shortcomings, and implementation costs is presented. The prime objective of this study is to highlight the prominence of utilizing user-defined programmable relays for modern DNs. Moreover, recommendations are presented by considering the application of user-defined relay characteristics that can be proved as a robust protection scheme to cope with the protection challenges in existing and future power systems developments.

The integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method outperforms other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33-bus network and 69-bus network by 29.20% and 27.88%, respectively.

This letter presents a 22-bit data-dense chipless radio frequency identification (RFID) tag with a multiparameter sensing feature. The tag has a radius of 7.4 mm, and it is excited with dual-polarized incident plane waves. Kapton® HN-based tag is loaded with copper as a radiating material and multiwall carbon nanotubes (MWCNTs) as a CO2 sensor. The slots are finely etched inside the metal sheet having a width of 0.2 mm. The frequency response of the tag is seen in the 4–25-GHz band. The rise of humidity level yields a change in the electrical properties of the Kapton® HN substrate that is observed in the frequency response of tag in terms of resonance drifting. In contrast, a change in CO2 gas concentration yields a difference in the chemical composition of nanotubes that are observed in the frequency resonance of the innermost slot. The tag exhibits a reliable behavior in extreme atmospheric conditions and returns to its preliminary state. This reversible nature of the proposed multiparameter sensor makes it an economical and reliable alternative to commercial environmental sensors. [ABSTRACT FROM AUTHOR]

Smartphones are a promising platform for continuous monitoring of human behavior. However, the ability to capture people’s behavioral patterns in-the-wild is a challenge, as the user’s behavior and physical activities can vary, given the variability of settings and environments. Modeling and understanding of human activity in-the-wild must not overlook a user’s behavioral context, which is just as crucial as recognizing the range of physical activities. The work in this paper presents a novel framework for context-aware human activity recognition by incorporating human behavioral contexts with physical activities. The proposed framework utilizes a series of machine learning classifiers to validate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]