19 research outputs found
Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
No full textThis paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller
Machine Intelligence in Smart Buildings
No full textEnergy efficiency is a key concern in achieving sustainability in modern society [...
Online Tuning of a PID Controller with a Fuzzy Reinforcement Learning MAS for Flow Rate Control of a Desalination Unit
No full textThis paper proposes a hybrid Zeigler-Nichols (Z-N) fuzzy reinforcement learning MAS (Multi-Agent System) approach for online tuning of a Proportional Integral Derivative (PID) controller in order to control the flow rate of a desalination unit. The PID gains are set by the Z-N method and then are adapted online through the fuzzy Q-learning MAS. The fuzzy Q-learning is introduced in each agent in order to confront with the continuous state-action space. The global state of the MAS is defined by the value of the error and the derivative of error. The MAS consists of three agents and the output signal of each agent defines the percentage change of each gain. The increment or the reduction of each gain can be in the range of 0% to 100% of its initial value. The simulation results highlight the performance of the suggested hybrid control strategy through comparison with the conventional PID controller tuned by Z-N
Intelligent Coordinator of Fuzzy Controller-Agents for Indoor Environment Control in Buildings Using 3-D Fuzzy Comfort Set
No full textElectric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm
No full textThe electrification of vehicles is considered to be the means of reducing the greenhouse gas (GHG) emissions of the transport sector, but “range anxiety” makes most people reluctant to adopt electric vehicles (EVs) as their main method of transportation. Workplace charging has been proven to counter range anxiety and workplace charging is becoming quite common. A workplace parking lot can house hundreds of EVs. In this paper, a program has been developed in MATLAB that uses the well-known evolutionary optimization algorithm, the genetic algorithm (GA), to optimize the charging schedule of fifty EVs that aims at achieving three goals: (a) keeping the electricity demand low, (b) reducing the cost of charging and (c) applying load shifting. Three schedules were developed for three scenarios. The results demonstrate that each schedule was successful in achieving its goal, which means that scheduling the charging of a fleet of EVs can be used as a method of demand-side management (DSM) in workplace parking lots and at the same time reduce the energy cost of charging. In the scenarios examined in this paper, cost was reduced by approximately 2%
Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network
No full textSince accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries generated from a simulated healthcare facility. ANFIS and backpropagation-based trained models outperformed all other models since they both deal well with complex nonlinear problems. LSTM also performed adequately. The models trained with metaheuristic algorithms demonstrated poor performance
An ANFIS-Fuzzy Tree-GA Model for a Hospital’s Electricity Purchasing Decision-Making Process Integrated with Virtual Cost Concept
No full textIn deregulated electricity markets, accurate load and price prediction play an essential role in the Demand Response (DR) context. Although electrical load and price demonstrate a strong correlation which is not linear, price prediction may be a task much more challenging than load prediction due to several factors. The volatility of electricity price compared to load makes price prediction a complex procedure. To perform purchasing decisions commercial consumers may rely on short term price and load prediction. A system which combines Adaptive Neuro-Fuzzy Systems (ANFIS) which predict Load Marginal Prices (LMPs) and electricity consumption is presented in this study. Furthermore, the Virtual Cost (VC) concept, which is the sum of the products between the predicted hourly consumption values and their respective predicted LMPs is introduced. Virtual Cost is assessed with a Fuzzy Decision Tree (FDT) compared to a threshold set by the customer. If needed, the amount of electrical energy that a healthcare facility must purchase at every hour of the day may be scheduled using Genetic Algorithm (GA) to meet the threshold criterion. This hybrid model proved economically beneficial for the facility, which is of great importance since the saved resources may be utilized to improve its infrastructures or for other purposes with social impact. The novelty of the proposed method is the utilization of ANFIS, Fuzzy Decision Trees and Genetic Algorithms combined as tools to improve the hospital’s energy and economic efficiency, achieving a reduction of the electricity costs up to 21.95 percent. The contribution of the study is to provide a reliable decision-making tool to everyone who participates in the electricity market in order to perform profitable energy scheduling automatically and accurately
