Optimization of Energy in Smart Farms Using a Genetic Algorithm

2022 ◽  
pp. 180-190
Author(s):  
Brahim Lejdel
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Smart Grid ◽  
Electrical Energy ◽  
Optimization Methods ◽  
Integrated Model ◽  
Energy Market ◽  
Great Demand ◽  
Agricultural Machines

The need for energy in the agriculture field is increasing as a result of increasing the productivity of these fields. The appearance of smart grid and IoT (internet of objects) enabled farmers to control, manage, and optimize the energy consumption. Agriculture will continue to rely on energy to increase its productivity in line with increasing population and great demand. In this chapter, the authors present an integrated model between SmartFarms, the smart-grid, and optimization methods. In this way, smart forms can participate actively and benefit from the energy market. In this chapter, they consider the electrical energy that is directly used in activities ranging from field processes such as irrigation of land. Energy is also indirectly consumed in synthetic additives notably fertilizers, pesticides, and herbicides. In addition, the authors can consider the electrical energy, which is used for powering some agricultural machines.

scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

scholarly journals FORECASTING OF ELECTRICAL ENERGY CONSUMPTION OF HOUSEHOLDS IN A SMART GRID

2021 ◽  
Vol 11 (6) ◽  
pp. 221-233
Author(s):  
Felix Ghislain Yem Souhe ◽  
Camille Franklin Mbey ◽  
Alexandre Teplaira Boum ◽  
Pierre Ele
Keyword(s):  
Energy Consumption ◽  
Smart Grid ◽  
Electrical Energy ◽  
Electrical Energy Consumption

scholarly journals A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2244 ◽  
Author(s):  
Ghulam Hafeez ◽  
Khurram Saleem Alimgeer ◽  
Zahid Wadud ◽  
Zeeshan Shafiq ◽  
Mohammad Usman Ali Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Convergence Rate ◽  
Smart Grid ◽  
Energy Management ◽  
Electrical Energy ◽  
Features Selection ◽  
Electrical Energy Consumption ◽  
Efficient Energy ◽  
Proposed Model

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.

scholarly journals Genetic Algorithm for Energy Commitment in a Power System Supplied by Multiple Energy Carriers

2020 ◽  
Vol 12 (23) ◽  
pp. 10053
Author(s):  
Mohammad Dehghani ◽  
Mohammad Mardaneh ◽  
Om P. Malik ◽  
Josep M. Guerrero ◽  
Carlos Sotelo ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Demand ◽  
Power Plants ◽  
Human Life ◽  
Electrical Energy ◽  
Energy System ◽  
Quality Level ◽  
Energy Carriers ◽  
Carrier Energy

In recent years, energy consumption has notably been increasing. This poses a challenge to the power grid operators due to the management and control of the energy supply and consumption. Here, energy commitment is an index criterion useful to specify the quality level and the development of human life. Henceforth, continuity of long-term access to resources and energy delivery requires an appropriate methodology that must consider energy scheduling such as an economic and strategic priority, in which primary energy carriers play an important role. The integrated energy networks such as power and gas systems lead the possibility to minimize the operating costs; this is based on the conversion of energy from one form to another and considering the starting energy in various types. Therefore, the studies toward multi-carrier energy systems are growing up taking into account the interconnection among various energy carriers and the penetration of energy storage technologies in such systems. In this paper, using dynamic programming and genetic algorithm, the energy commitment of an energy network that includes gas and electrical energy is carried out. The studied multi-carrier energy system has considered defending parties including transportation, industrial and agriculture sectors, residential, commercial, and industrial consumers. The proposed study is mathematically modeled and implemented on an energy grid with four power plants and different energy consumption sectors for a 24-h energy study period. In this simulation, an appropriate pattern of using energy carriers to supply energy demand is determined. Simulation results and analysis show that energy carriers can be used efficiently using the proposed energy commitment method.

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