Recent advances in Resistive RAM (RRAM) based Computation-In-Memory (CIM) architectures highlight significant potential for accelerating data-intensive computing tasks. However, non-idealities in RRAM devices, such as variability, result in small sensing margins that can significantly affect the computational efficiency. This issue becomes even more pronounced when dealing with complex multi-operand logic operations. This paper introduces a circuit-level scheme for CIM-based multi-operand XOR logic operations, leveraging a Voltage-To-Time converter (VTC) to perform multi-phased XORs in a single clock cycle. In this approach, we exploit bitline capacitances for voltage-based sensing during computation, generating an output voltage that is linearly proportional to the operand values. This voltage is then converted into the desired logic output using the VTC design. Furthermore, low-power techniques are employed in the deployment of sense amplifiers, such as regulating power consumption during operation and disabling the amplifiers once the decision is made. Simulation results for a post-layout extracted 512x512 (256Kb) RRAM-based CIM array show that up to 16-operand XOR operation can be accurately and reliably performed as opposed to a maximum of three operands supported by state-of-the-art solutions, while offering up to 49× better figure-of-merit combining energy-efficiency and throughput.

Electricity theft can lead to enormous economic losses and cause operational and security problems for electricity networks and utilities. Most current research has focused on electricity theft detection in the consumption sector. However, the high penetration rate of distributed generation (DG) can lead to an increase in power theft attacks in this sector via smart meter manipulation. This study is an extension of prior works focused on electricity theft detection in the consumption and generation domains of a smart grid environment with DG. This study proposes a novel electricity theft detection framework based on quantum machine learning (QML). The elegant field of QML has been used to demonstrate that data classification becomes more efficient in higher-dimensional spaces. An extensive numerical study was conducted to determine the type of QML architecture that can perform well and efficiently in electricity theft detection cases. The technique presented here has not yet been extensively studied in the domain of energy theft detection. Extensive experiments were conducted to assess this approach, and an accuracy of 0.87 was achieved with respect to the classical consumption domain, whereas an accuracy of 0.977 was achieved with respect to the net metering domain.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of “classical” and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the “features” (intended as “time steps”), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.