THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

Photovoltaic (PV) is one of the most promising renewable energy sources. To ensure secure operation and economic integration of PV in smart grids, accurate forecasting of PV power is an important issue. In this paper, we propose the use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems. The LSTM networks can model the temporal changes in PV output power because of their recurrent architecture and memory units. The proposed method is evaluated using hourly datasets of different sites for a year. We compare the proposed method with three PV forecasting methods. The use of LSTM offers a further reduction in the forecasting error compared with the other methods. The proposed forecasting method can be a helpful tool for planning and controlling smart grids.

Accurate photovoltaic power forecasting models using deep LSTM-RNN

Distribution System Modeling and Analysis

Whale Optimization Algorithm (WOA) is an optimization algorithm developed by Mirjalili and Lewis in 2016. An overview of WOA is described in this paper, rooted from the bubble-net hunting strategy, besides an overview of WOA applications that are used to solve optimization problems in various categories. The best solution has been determined to make something as functional and effective as possible through the optimization process by minimizing or maximizing the parameters involved in the problems. Research and engineering attention have been paid to Meta-heuristics for purposes of decision-making given the growing complexity of models and the needs for quick decision making in the engineering. An updated review of research of WOA is provided in this paper for hybridization, improved, and variants. The categories included in the reviews are Engineering, Clustering, Classification, Robot Path, Image Processing, Networks, Task Scheduling, and other engineering applications. According to the reviewed literature, WOA is mostly used in the engineering area to solve optimization problems. Providing an overview and summarizing the review of WOA applications are the aims of this paper.

A comprehensive survey: Whale Optimization Algorithm and its applications

An efficient analytical (EA) method is proposed for optimally installing multiple distributed generation (DG) technologies to minimize power loss in distribution systems. Different DG types are considered, and their power factors are optimally calculated. The proposed EA method is also applied to the problem of allocating an optimal mix of different DG types with various generation capabilities. Furthermore, the EA method is integrated with the optimal power flow (OPF) algorithm to develop a new method, EA-OPF which effectively addresses overall system constraints. The proposed methods are tested using 33-bus and 69-bus distribution test systems. The calculated results are validated using the simulation results of the exact optimal solution obtained by an exhaustive OPF algorithm for both distribution test systems. The results show that the performances of the proposed methods are superior to existing methods in terms of computational speed and accuracy.

Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization

Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT The proposed method is verified and tested through the simulation of a grid-connected PV system model The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions

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An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite–Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.

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Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

https://www.mdpi.com/1424-8220/21/4/1038/pdf

Karar Mahmoud

Papers

Accurate photovoltaic power forecasting models using deep LSTM-RNN

Optimal Distributed Generation Allocation in Distribution Systems for Loss Minimization

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.