Energy forecasting is important because it enables infrastructure planning and power dispatching while reducing power outages and equipment failures. It is well-known that federated learning (FL) can be used to build a global energy predictor for smart grids without revealing the customers’ raw data to preserve privacy. However, it still reveals local models’ parameters during the training process, which may still leak customers’ data privacy. In addition, for the global model to converge, it requires multiple training rounds, which must be done in a communication-efficient way. Moreover, most existing works only focus on load forecasting while neglecting energy forecasting in net-metering systems. To address these limitations, in this article, we propose a privacy-preserving and communication-efficient FL-based energy predictor for net-metering systems. Based on a data set for real power consumption/generation readings, we first propose a multidata-source hybrid deep learning (DL)-based predictor to accurately predict future readings. Then, we repurpose an efficient inner-product functional encryption (IPFE) scheme for implementing secure data aggregation to preserve the customers’ privacy by encrypting their models’ parameters during the FL training. To address communication efficiency, we use a change and transmit (CAT) approach to update local model’s parameters, where only the parameters with sufficient changes are updated. Our extensive studies demonstrate that our approach accurately predicts future readings while providing privacy protection and high communication efficiency.

Lead halide perovskite solar cell shows an enormous potential for the next generation renewable energy, but it also offers some environmental hazards due to their inherent lead-based toxicity. On the other hand, if lead based perovskite is efficiently replaced by other non-toxic metal based perovskite then their toxicity can remarkably be reduced for photovoltaic applications. In this study, a novel lead free compound Cs ₂ TiBr ₆ based photovoltaic device is proposed as Au/PEDOT:PSS/ Cs ₂ TiBr ₆ /TiO ₂ /AZO, where each layer is optimized and analysed through a series of simulations for maximum power-conversion efficiency using SCAPS-1D software. Comprehensive optimization of the thickness and doping density of each layer leads to the maximum power-conversion efficiency up to 17.83% for our proposed solar cell. Therefore, we believe that these results will provide a route heading towards the progress of lead-free and highly efficient perovskite photovoltaic devices.

Most of the existing smart parking systems threaten the drivers’ privacy by revealing information about their visited locations. Moreover, they are centralized making them vulnerable to a single point of failure and attack, which threatens the availability of the parking service. They also suffer from a lack of transparency, where the centralized service organizer may favor some parking lots by booking their parking slots first. To tackle these concerns, we propose a blockchain-based smart parking system with privacy preservation and reputation management. In our system, a consortium blockchain is created by different parking lots to run the parking system securely and transparently, where the parking offers are recorded on a shared and immutable ledger. We use a commitment technique during the submission of the offers to ensure fair parking rates. Then, we use a private information retrieval technique during the offers retrieval to preserve the drivers’ location privacy. Furthermore, to anonymously and efficiently authenticate drivers during the reservation process, we use a short randomizable signature. We also use a time-locked anonymous payment technique to discourage drivers from not committing to their parking reservations and provide a secure and privacy-preserving payment method for parking service. Finally, we integrate a blockchain-based anonymous reputation management scheme into our system, where drivers can anonymously rate the parking service to ensure high quality of service. Our evaluations demonstrate that our smart parking system is secure and capable of preserving drivers’ privacy with low communication, computation, and storage overheads.

In the advanced metering infrastructure, smart meters are deployed at the consumers’ side to regularly transmit fine-grained electricity consumption readings to the system operator (SO) for billing and real-time load monitoring and energy management. However, fraudulent consumers may compromise their meters to launch electricity-theft cyberattacks by reporting low-consumption readings to reduce their bills. These false readings not only cause financial losses but also degrade the grid’s performance because they are used for energy management and load estimate. The existing solutions in the literature focus only on securing the billing, so they are not designed to detect the attacks in real time, and thus the SO may use false readings for a long period of time in load monitoring and energy management until they are identified. In this paper, we propose a general ensemble-based deep-learning detector that enables the SO to detect false readings in real time. To do that, we first train several deep learning models on samples generated from a sliding window of the readings. Then, we use the best-performing model to train several models on different ratios of false readings and use them in our ensemble-based detector. Extensive experiments are conducted, and the results indicate that comparing to the literature, our detector can detect the false readings after sending a few false readings (around 15) comparing to the existing daily and weekly detection approaches that need 144 and 1,008 readings, respectively.

The smart grid advanced metering infrastructure (AMI) is vulnerable to electricity theft cyber-attacks in which malicious smart meters report low readings to reduce the consumers’ bills. To avoid this problem, several machine-learning-based detectors have been proposed to detect electricity theft. Most of these detectors are global in the sense that they are trained on different consumption levels, including low and high consumptions, to be used for all consumers. In this article, we introduce a novel type of evasion attacks against global detectors as follows. A malicious consumer who has high consumption level can send false readings for a low-consumption profile (that resembles the profiles the detector is trained on) to evade the detector, i.e., steal electricity without being detected. We first conduct experiments to prove that the existing global detectors are vulnerable to this new kind of evasion attacks. To launch this attack, we train a generative adversarial network (GAN) on a real data set to generate fake low-consumption readings that can evade the detector. The given results indicate that the success rate of the attack is between 82% and 97%. To thwart this attack, we divide the consumers into clusters of close electricity consumption levels and train one detector for each cluster. Therefore, if a malicious consumer in any cluster tries to imitate the consumption profiles of consumers in other clusters, he/she will be detected. On the other hand, it is not profitable to imitate the electricity consumption profiles of consumers in his/her cluster to evade detection. To prove the effectiveness of our countermeasure, extensive experiments are conducted and the results indicate that our countermeasure can successfully thwart the attack.

Among various photovoltaic devices, the poly 3, 4-ethylenedioxythiophene:poly styrenesulfonate (PEDOT:PSS) and silicon nanowire (SiNW)-based hybrid solar cell is getting momentum for the next generation solar cell. Although, the power-conversion efficiency of the PEDOT:PSS–SiNW hybrid solar cell has already been reported above 13% by many researchers, it is still at a primitive stage and requires comprehensive research and developments. When SiNWs interact with conjugate polymer PEDOT:PSS, the various aspects of SiNW array are required to optimize for high efficiency hybrid solar cell. Therefore, the designing of silicon nanowire (SiNW) array is a crucial aspect for an efficient PEDOT:PSS–SiNW hybrid solar cell, where PEDOT:PSS plays a role as a conductor with an transparent optical window just-like as metal-semiconductor Schottky solar cell. This short review mainly focuses on the current research trends for the general, electrical, optical and photovoltaic design issues associated with SiNW array for PEDOT:PSS–SiNW hybrid solar cells. The foremost features including the morphology, surface traps, doping of SiNW, which limit the efficiency of the PEDOT:PSS–SiNW hybrid solar cell, will be addressed and reviewed. Finally, the SiNW design issues for boosting up the fill-factor, short-circuit current and open-circuit voltage will be highlighted and discussed.