The electrification of transport has proved to be a breakthrough to uplift the sustainable and eco-friendly platform in the global sector in which electric vehicles (EVs) are considered indispensable. In particular, creating intelligent energy management in the power distribution system integrated with electric vehicle charging stations (EVCS) as a new entity is one of the most important challenging tasks. The implementation of the EVCS network infrastructure should facilitate the adoption of the spatiotemporal electricity demand for EVs. The intelligent decision for the transmission, distribution, energy allocation and charging station placement by the control center or central aggregator is only possible by correctly forecasting its usage, occupancy, and energy or charging demand. Techniques like data analytics have enabled to extract data from the EVCS on a daily basis to store and process all the recorded data. To overcome the above-mentioned challenges related to energy demand forecasting for EVCS network, this work proposes two encoder-decoder models based on convolutional long short-term memory networks (ConvLSTM) and bidirectional ConvLSTM (BiConvLSTM) in combination with the standard long short-term memory (LSTM) network. Data on energy demand from EVCS located in four different cities is used in the proposed models. All datasets are preprocessed to make them suitable for the multi-step time-series learning models in order to make the framework data-centric. The suggested architecture employs the ConvLSTM and BiConvLSTM to extract the key features from the spatiotemporal data of the energy demand data of the EVCS distributed over the time and space. The predicted outcomes generated by the suggested strategy are compared with conventional deep learning models and traditional machine learning techniques.

Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

Smart grid is expected to support electric vehicles parking lots with the existing power line infrastructure. In order to support all electric-vehicles (EVs) users to complete their charging needs before leaving the parking lot, the power grid requires that the charging demands of EVs should be within the allowable power limit to avoid the grid overloading. This paper proposes a fuzzy logic inference based algorithm (FLIA) to manage the available power efficiently for EVs in the parking lot. The problem is mathematically formulated and solved by the credibility of the fuzzy inference mechanism to control charging and discharging of the EVs. The key idea is to introduce the fuzzy inference mechanism that evaluates several uncertain input parameters from the electric grid and from EVs to obtain an adequately accurate charging or discharging decision for each of the connected EVs. The proposed scheme is applied to a parking lot with different parking capacities and compared with the conventional based systems. The simulation results demonstrated the feasibility and effectiveness of the proposed algorithm when dealing with the available power management and satisfying the EV user’s requirements.

There is a growing interest in increasing the penetration rate of renewable energy systems due to the drawbacks associated with the use of fossil fuels. However, the grid integration of renewable energy systems represents many challenging tasks for system operation, stability, reliability, and power quality. Small hybrid renewable energy systems (HRES) are small-scale power systems consisting of energy sources and storage units to manage and optimize energy production and consumption. Appropriate real-time monitoring of HRES plays an essential role in providing accurate information to enable the system operator to evaluate the overall performance and identify any abnormal conditions. This work proposes an internet of things (IoT) based architecture for HRES, consisting of a wind turbine, a photovoltaic system, a battery storage system, and a diesel generator. The proposed architecture is divided into four layers: namely power, data acquisition, communication network, and application layers. Due to various communication technologies and the missing of a standard communication model for HRES, this work, also, defines communication models for HRES based on the IEC 61850 standard. The monitoring parameters are classified into different categories, including electrical, status, and environmental information. The network modeling and simulation of a university campus is considered as a case study, and critical parameters, such as network topology, link capacity, and latency, are investigated and discussed.

Nowadays, conventional agriculture farms lack high-level automated management due to the limited number of installed sensor nodes and measuring devices. Recent progress of the Internet of Things (IoT) technologies will play an essential role in future smart farming by enabling automated operations with minimum human intervention. The main objective of this work is to design and implement a flexible IoT-based platform for remote monitoring of agriculture farms of different scales, enabling continuous data collection from various IoT devices (sensors, actuators, meteorological masts, and drones). Such data will be available for end-users to improve decision-making and for training and validating advanced prediction algorithms. Unlike related works that concentrate on specific applications or evaluate technical aspects of specific layers of the IoT stack, this work considers a versatile approach and technical aspects at four layers: farm perception layer, sensors and actuators layer, communication layer, and application layer. The proposed solutions have been designed, implemented, and assessed for remote monitoring of plants, soil, and environmental conditions based on LoRaWAN technology. Results collected through both simulation and experimental validation show that the platform can be used to obtain valuable analytics of real-time monitoring that enable decisions and actions such as, for example, controlling the irrigation system or generating alarms. The contribution of this article relies on proposing a flexible hardware and software platform oriented on monitoring agriculture farms of different scales, based on LoRaWAN technology. Even though previous work can be found using similar technologies, they focus on specific applications or evaluate technical aspects of specific layers of the IoT stack.

The sizing problem of the hybrid energy system (HES) is a crucial issue especially in rural communities because any wrong results can mislead the decision makers for building the new HES. Due to the intermittent nature of the renewable energy sources (RES) such as wind and PV, there will be a need for a high storage system and/or a standby diesel engine, which increase the investment, required, and increases the cost of energy (CoE). The use of smart grid concepts like the demand response (DR) using dynamic tariff can improve the system performance, enhance the stability, reduces the size and investments of HES components, reduces the customers' bills, and increases the energy providers' profits. The DR strategy will allow the customers to share the responsibility of the HES stability with the energy providers to maintain the stability of the HES. The DR strategies should be selected to ensure the balance between the available RES and the load requirements. In this article, a novel DR strategy is introduced to model the required change in the tariff with the battery state of charge and its charging/discharging power. The novel DR strategy is used in the sizing of the HES based on techno-economic objectives using three different soft computing optimization techniques. This article introduces modeling and simulation of the smart grid integrated with hybrid energy systems to supply a standalone load for a rural site in the north of Saudi Arabia. The sizing of the HES is built based on minimizing the CoE and the loss of load probability. The novel DR strategy introduced in this article reduced the size of the HES compared to the fixed load technique by 20.66%. The results obtained from this novel strategy proved its superiority in the sizing and operation stage of the HES.