This paper describes the development trends and prospects of green-energy-based off-road electric vehicles and robots in the agricultural sector. Today, the agriculture sector faces several challenges, such as population growth, increasing energy demands, labor shortages, and global warming. Increases in energy demand cause many challenges worldwide; therefore, many methods are suggested to achieve energy independence from fossil fuels and reduce emissions. From a long-term point of view, the electrification of agricultural vehicles and renewable energy sources appear to be an essential step for robotic and smart farming in Agriculture 5.0. The trend of technological growth using fully autonomous robots in the agricultural sector seems to be one of the emerging technologies to tackle the increased demand for food and address environmental issues. The development of electric vehicles, alternative green fuels, and more energy-efficient technologies such as hybrid electric, robotic, and autonomous vehicles is increasing and improving work quality and operator comfort. Furthermore, related digital technologies such as advanced network communication, artificial intelligence techniques, and blockchain are discussed to understand the challenges and opportunities in industry and research.

Previous studies have shown that electric water heaters (EWH) have strong potential in demand-side management applications, more precisely because they offer energy storage capability and then can be employed as shift loads. However, the challenge of EWH curtailment strategies is to minimize the impact on the hot water availability while shaving the peak of consumption during critical periods. The success of such strategies depends highly on the knowledge of the consumption behavior of each user. Thus, appropriated modeling and consumption analysis could yield better management strategies. This study proposes an electric water heater control strategy based on dynamic programming and power consumption profile classification. An adaptive clustering process allows recognizing the clients who contribute to the highest power consumption during the peak periods. The analysis and simulation indicate that an appropriate control on the group of users could be implemented to reduce peak demand and to meet the hot water demand. A K-means clustering algorithm has been used for cluster analysis. The silhouette method has been applied to estimate the appropriate number of clusters.

Paralleled operation of voltage-source inverters (VSIs) is currently achieved by using voltage/frequency droop control techniques which requires the knowledge of the system parameters. Otherwise, centralized control techniques with robust communication among VSIs controllers are also used. This paper presents a new control strategy which allows the load sharing between the power sources of an ac microgrid without centralized controller or any communication among the VSIs; only local measurements of voltage and output current are used. The dispatchable sources (e.g., fuel cells) of the microgrid are operated using voltage control with a direct droop scheme, and the nondispatchables or intermittent ones (e.g., wind turbine generators) are operated using power control with a complementary inverse droop scheme (D-Droop + I-Droop). The number of operating sources can be changed online without any modification needed on the VSI controllers. The proposed VSI controllers are based on the variable frequency adaptive linear neuron with frequency-locked loop for the VSIs system synchronization, voltage/power and signal estimation. Experimental results using field-programmable gate array devices for the implementation of each VSI control in the microgrid test bench demonstrated the validity of the proposition.

Anomaly detection is a significant application of residential appliances load monitoring systems. As an essential prerequisite of load diagnosis services, anomaly detection is critical to energy saving and occupant comfort actualization. Notwithstanding, the investigation into diagnosis of household anomalous appliances has not been decently taken into consideration. This paper presents an extensive study about operation-time anomaly detection of household devices particularly, refrigerators, in terms of appliances candidate, by utilizing their energy consumption data. Energy as a quantitative property of electrical loads, is a reliable information for a robust diagnosis. Additionally, it is very practical since it is low-priced to measure and definite to interpret. Subsequently, an on-line anomaly detection approach is proposed to effectively determine the anomalous operation of the household appliances candidate. The proposed approach is capable of continuously monitoring energy consumption and providing dynamic information for anomaly detection algorithms. A machine learning-based technique is employed to construct efficient models of appliances normal behavior with application to operation-time anomaly detection. The performance of the suggested approach is evaluated through a set of diagnostic tests, by utilizing normal and anomalous data of targeted devices, measured by an acquisition system. In addition, a comparison analysis is provided in order to further examine the effectiveness of the developed mechanism by exploiting a public database. Moreover, this study elaborates sensible remarks on an effective management of anomaly detection and diagnosis decision phases, pivotal to correctly recognition of a faulty/abnormal operation. Indeed, through experimental results of case studies, this work assists in the development of a load monitoring and anomaly detection system with practical implementation.