Uni-Variate and Multi-Variate Short-Term Household Electricity Consumption Prediction Using Machine Learning Technique

Background: Electricity consumption prediction plays an important role in conservation, development, and future planning. Accurate prediction model has various field applications in real-life scenarios, future electricity demand estimation, performance evaluation of current time, fault detection, efficient energy production, resource-saving, and many more. In this paper, a CNN based short term building electricity consumption prediction model is developed and tested for two different types of datasets that can perform weekly prediction. Two different datasets are used to check how the algorithm behaves on different datasets i.e., what are the impacts dataset has on prediction accuracy. Errors were calculated using MAE and RMSE. Objective: The objective of the study is to develop an electricity consumption prediction (ECP) model for a univariate and multivariate dataset using CNN and LSTM network and to find that how the correlation and independency of features affect the electricity prediction task. Methods: The proposed electricity consumption model is built using the deep CNN andLSTM network and is trained and tested using the univariate and multivariate time series dataset thus the two experiments have been performed and are named as U-ECPCL (Univariate- Electricity Consumption Prediction using CNN and LSTM) and M-ECPCL (Multivariate- Electricity Consumption Prediction using CNN and LSTM) respectively. Results: The model predicts accurately with few errors with MAE of 0.251 and RMSE of 0.66 for univariate dataset and MAE of 4.36 and RMSE of 11.53 for a multivariate dataset. Conclusion: The model predicts accurately with few errors and if the prediction error of univariate and multivariate are compared then it is concluded that the univariate model outperforms the multivariate model.

Recent Advances in Robot Trajectory Planning in a Dynamic Environment

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.

Energy Efficiency in IoT based on Sensor Node Deployment Pattern

: IoT becomes more complicated due to its large size. The existing techniques of Wireless Sensor Networks (WSN) are not useful directly to the IoT. That’s why the using the energy efficient schemes for the IoT is a challenging issue. Due to battery constrained IoT devices, energy efficiency is of greatest importance. This paper gives overview and broad survey on IoT, WSN in IoT, Challenges in IoT and WSN, energy conserving issues and solutions and different Node Deployment patterns. For green IoT, this paper addresses energy competence issues by proposing an energy efficient heuristic for a regular and particular deployment scheme. QC-MCSC heuristic is implemented for Strip Based Deployment Pattern and analyzed in terms of Energy Efficiency and Life Time of a sensor on Energy Latency Density Design Space, a topology management application that is power efficient. QC-MCSC for Strip based deployment pattern and for random deployment pattern are compared.

Recent Patents on Vision Technology-Based Devices

Background: In VTBDs (Vision Technology-Based Devices), vision technology is utilized to acquire abundant information about the external environment and process such information to achieve certain functions. They are used in various fields to solve practical problems. Various patents have been discussed in this article, hoping to provide ideas for solving practical problems in the future. Objective: The study aimed to provide an overview of the existing VTBDs and introduce their classifications, characteristics, as well as the stage and trend of development. Methods: This paper reviews various patents, especially Chinese patents related to VTBDs. The structural characteristics, differentiation, and engineering applications of VTBDs are also introduced. Results: The existing VTBDs are analyzed and compared, and their typical characteristics are summarized. The main applications, as well as the pros and cons, in the current development stage, are summarized and analyzed, as well. In addition, the development trend of VTBDs-related patents is also discussed. Conclusion: VTBDs can be categorized into DsBMV (Devices Based on Monocular Visual), DsBBV (Devices Based on Binocular Visual), and DsBMCV (Devices Based on Multi-Camera Visual). All of these categories exhibit their own relative advantages and disadvantages. Therefore, it is of much importance to analyze the specific problems, followed by selecting appropriate machine vision technologies and reasonable mechanical structures to design VTBDs accordingly.