Solar energy is one of the main renewable energies available to fulfill global clean energy targets. The main issue of solar energy like other renewable energies is its randomness and intermittency which affects power grids stability. As a solution for this issue, energy storage units could be used to store surplus energy and reuse it during low solar generation intervals. Also, in order to sustain stable power grid and better grid operation and energy storage management, photovoltaic (PV) power forecasting is inevitable. In this paper, new hybrid model based on deep learning techniques is proposed to predict short-term PV power generation. The proposed model incorporates convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder network. The new model differentiates itself in accomplishing high prediction accuracy by extracting spatial features in time series via CNN layers and temporal features between the time series data through LSTM. The introduced model is tested on dataset of power generation from southern UK solar farm and the weather data corresponding to same location and time intervals; the forecasting performance of the suggested model is evaluated in metrics of root-mean-square error (RMSE) and mean absolute error (MAE). The used model is compared with different models from the literature either of pure type of network such as LSTM and gated recurrent unit (GRU) or hybrid combination of different networks like CNN-LSTM and CNN-GRU. The results show that proposed model provides enhanced results and reduces training time significantly compared to other competitive models, where the performance of the proposed model improved averagely by 5% to 25% in terms of RMSE and MAE performance metrics, and the execution time of training significantly reduced with almost 70% less compared to other models.

Optimal Tuning of PID Controller using Adaptive Hybrid Particle Swarm Optimization Algorithm

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.

In last few years, integrating renewable energy sources (RES) with traditional energy sources became prominent applications to diversify the energy mix in the modern power grids. Nevertheless, the incorporation of RES poses additional challenges to power distribution networks because of their inherent intermittency and their randomness nature. Furthermore, maintaining grid operational efficiency during peak demand periods requires effective peak load shaving, which remains a critical challenge. A promising remedy of these challenges is to use energy storage technologies such as battery systems, green hydrogen generation, thermal storage, etc. Thus, the battery stored energy can be dispatched during periods of low renewable generation and high load demand, thereby enhancing grid reliability and efficiency. Consequently, energy storage solutions emerge as a compelling alternative to traditional, expensive grid challenges, due to their flexibility, declining costs, and rapid deployment. This paper proposes a novel hybrid algorithm for optimizing the charging and discharging schedule of a PV-battery storage system connected to microgrid. The proposed Hybrid optimization technique combines two metaheuristic algorithms, Particle swarm optimization (PSO) and gray wolf optimization (GWO) for utilizing the available PV power optimally targeting the reduction of the peak power demand. Historical data of solar PV generation unit and power demand are collected from a substation in UK. The proposed approach introduces a new optimization strategy that maximizes the use of photovoltaic energy to charge the energy storage unit which contributes to maximizing the daily peak load reduction. The simulation results conducted for two seasonal scenario days with different PV generation profiles achieve power load reductions between 21.6% and 28.1%. The results also prove that the proposed algorithm outperforms the other well-known optimization techniques introduced in the literature in terms of optimization techniques performance metrics with ranges from 1 to 3%.