Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.

A review on time series forecasting techniques for building energy consumption

The concept of a semi-autonomous adaptive cruise control (SAACC) system is developed, which enjoys significant advantages over present day adaptive cruise control (ACC) systems in terms of highway safety and traffic flow capacity. The semi-autonomous systems combine the deployment advantages of autonomous vehicles with the performance advantages of fully automated highway systems (AHSs) in which vehicles operate cooperatively as a platoon. Unlike platoon systems, the semi-autonomous systems are immediately deployable on present day highways, where both manually driven and adaptive cruise controlled vehicles can coexist. Our theoretical results show that the proposed system is able to maintain smaller time gaps safely, is string stable, and is guaranteed to have smaller actuator inputs than a standard autonomous ACC system. The simulation results indicate that more accurate and smoother tracking, smaller time gaps, smaller control efforts, and increased robustness to vehicle dynamics are achieved by semi-autonomous control.

Semi-autonomous adaptive cruise control systems

Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders

This paper develops a framework for the design and evaluation of spacing policies for adaptive cruise control. Spacing policies are evaluated from the point of view of string stability, traffic flow stability and traffic flow capacity. The standard constant time-gap spacing policy can guarantee string stability but is shown to suffer from poor traffic capacity and traffic flow instability. An "ideal" spacing policy is proposed that evolves naturally from the evaluation framework. The proposed spacing policy is a nonlinear function of speed. It provides string stability and traffic flow stability as well as a higher traffic flow capacity compared to the standard time-gap controller. An associated result proved in the paper is that traffic flow stability implies string stability for flow volumes up to a described maximum value.

On spacing policies for highway vehicle automation

In recent years, road vehicle automation has become an important and popular topic for research and development in both academic and industrial spheres New developments have received extensive coverage in the popular press, and it may be said that the topic has captured the public imagination Indeed, the topic has generated interest across a wide range of academic, industry and governmental communities, well beyond vehicle engineering; these include computer science, transportation, urban planning, legal, social science and psychology While this follows a similar surge of interest - and subsequent hiatus - of Automated Highway Systems in the 1990s, the current level of interest is substantially greater, and current expectations are high It is common to frame the new technologies under the banner of self-driving cars - robotic systems potentially taking over the entire role of the human driver, a capability that does not fully exist at present However, this single vision leads one to ignore the existing range of automated systems that are both feasible and useful Recent developments are underpinned by substantial and long-term trends in computerisation of the automobile, with developments in sensors, actuators and control technologies to spur the new developments in both industry and academia In this paper, we review the evolution of the intelligent vehicle and the supporting technologies with a focus on the progress and key challenges for vehicle system dynamics A number of relevant themes around driving automation are explored in this article, with special focus on those most relevant to the underlying vehicle system dynamics One conclusion is that increased precision is needed in sensing and controlling vehicle motions, a trend that can mimic that of the aerospace industry, and similarly benefit from increased use of redundant by-wire actuators

/pdf/automated-driving-and-autonomous-functions-on-road-vehicles-19qsjg4ldo.pdf

Automated driving and autonomous functions on road vehicles

This paper describes a intelligent control system for cruise driving vehicle. With the steady increase in the number of automobiles on the road, it has become ever more important to merge traffic flow efficiently to optimize utilization of existing road capacity. Based on the idea that minimize fuel cost air pollution and accept driver's need for driving, we have developed a intelligent control system for cruise driving vehicle. The system with intelligent controller for cruise driving is fully implemented in a open road and verify its performance.

Development of intelligent cruise control system

In this paper, I-PDA controller based on Coefficient Diagram Method incorporating feedforward controller is applied by robotic manipulators. Robotic manipulator models contain uncertain elements, which are not known exactly. Therefore, the dynamics of robotic manipulators are generally classified as uncertain dynamic system. The controller considered for the robotic manipulators need to move payloads of different masses from one point to another with good balance of the stability and response, consequently we propose I-PDA controller based on Coefficient Diagram Method incorporating FFC. The effectiveness of the controller for different system type of robotic manipulators is demonstrated by the simulation results.

http://2005.iccas.org/submission/paper/upload/1i-pda controller design for robotic manipulator based on coefficient diagram method with ffc.pdf

I-PDA controller design for Robotic Manipulator based on Coefficient Diagram Method with FFC

We desire to design a controller using intelligence algorithms, making a container crane control systems simulator. Trolley and lifts standard velocity is modeled using a fuzzy model expressing complex systems input/output simply and exactly. We also desire to design a structure the driving conditions are: 1) loading volume 2) wheel span 3) pole inside gap 4) pole outside gap 5) loading height 6) wheel base 7) lifting velocity 8)trolley covering velocity 9) crane covering velocity 10) trolley driving equipment and 11) cycle time.

Design of container crane controller using intelligence algorithms

The goal of this paper is to improve the performance of a system which recognizes the license plate. In this paper, we suggest the system using composite color and an artificial hippocampus algorithm for license plate recognition. Before extraction with image, the system first operates histogram equalization in pre-processing for the equal distribution of intensity value. After the pre-processing, the system extracts the license plate region using color models which are RGB, HSI and YIQ. The extracted region again is extracted to a letter/number region using horizontal and vertical histograms. Therefore, the system recognizes the letter/number of license plate using artificial hippocampus algorithm. Experimental results with input images are reported to demonstrate the accuracy and robustness of the proposed algorithm.

The Performance Improvement of License Plate Recognition System Using Composite Color and Artificial Hippocampus Algorithm

We propose a chaotic fuzzy controller to analyze the chaotic character of time series obtained from a specific plant and to predict the short-term for electric power demand of the plant using the fuzzy controller. We compare the predicted data with the active ones and check the error generated by them after we obtain time series of supplied electric power to the proposed controller. As a result of the simulation, we obtained the consequence that the proposed controller can be advanced through various and accurate data acquisition, and continuous analysis of the residential and industrial environment.

A predictive demand of the maximum electric power using chaos-fuzzy

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