Index

  • 1Soltech 1STH-215-p solar PV module, 122
  • Accountability, of personnel, 235
  • Adam as optimizer, 220
  • Adaptive neuro-fuzzy inference system (ANFIS), 162
    • defuzzifying layer, 165
    • fuzzifying layer, 164
    • implication layer, 164
    • normalization layer, 164
    • output layer, 165
  • Advanced metering infrastructure, 201
  • Aggregation mechanisms, cluster-based, 82
  • Agreement on Agriculture (AoA), WTO, 234
  • AI, 183
  • Air conditioners, discovery of, 212–213
  • Air density, 181
  • Amazon, 236
  • Ami attack scenario,
    • distributed denial of service, 204
    • packet flooding, 204
    • spoofing, 204
  • ANN, 185
  • Ant colony optimization, 103, 104
  • AODV protocol, 81
  • ARIMA, 185
  • Artificial bee colony (ABC), 37, 58
  • Artificial intelligence (AI), 37–38, 62, 183
  • Artificial intelligence (AI) in production of biodiesel energy,
    • biodiesel in India context, 233–234
    • Indian perspective of renewable biofuels, 230–232
    • opportunities, 232–233
    • overview, 230
    • proposed model, 234–236
    • role, 235
  • Artificial intelligence (AI)–based energy-efficient clustering and routing in IoT–assisted WSNs,
    • clustering, 84–85
    • overview, 80
    • related study, 81–84
    • research methodology, 85–88
      • AI-based energy-aware routing protocol, 87–88
      • clustering approach, 86–87
      • WSN–based IoT environment, 85–86
  • Artificial intelligence method, 158–160
    • activation function, 161
    • adoptive linear element neural network, 160
    • feed-forward neural network (FFNN), 160
    • mean, 161
    • radial basis function neural network (RBFNN), 160
    • recurrent neural network, 160
    • ridgelet neural network, 160
    • sigmoidal function, 161
    • standard deviation, 161
    • wavelet neural network, 161
    • weights, 161
  • Artificial neural network (ANN), 38
  • Atmanirbhar Bharat, 232
  • Autoencoders, 130, 139–140, 144
  • Aviation turbine fuel (ATF), 232
  • Back-propagation multilayer perceptron (MLP), 37
  • Base station (BS), 81, 83
  • Batch learning, 135, 136
  • Battery SoC modeling using polynomial regression,
    • current research in battery energy storage systems, 118
    • dynamic battery modeling, 119–122
      • block diagram for simulation setup, 120
      • proposed methodology, 120–122
    • first-order battery model, 119
    • overview, 115–119
    • results and discussion, 122–126
  • Bayesian regularization (BR), 42
    • Levenberg-Marquardt (LM), 37
  • Bayesian ridge regression, forecasting of electricity consumption, 217, 220, 221
  • Betz’s limit, 152
  • Bias weights, 39
  • Bidirectional LSTM, 181, 185, 194, 216, 219, 220, 221–222
  • Bidirectional recurrent neural networks (BRNNs), 216, 219, 220, 221–222
  • Bi-LSTM, 181–182, 185, 194, 197
  • Biodiesel energy,
    • AI in production of (see Artificial intelligence (AI) in production of biodiesel energy)
    • classification, 230, 231
    • cost of, 233–234
    • ethanol, 232, 235, 236
    • extraction in India, 235
    • fish oil methyl ester, 232
    • in commercial flights, 232
    • in India context, 233–234
    • Indian perspective of renewable biofuels, 230–232
    • storage, 233
  • Biomethanization, 94
  • BLM, 185
  • Blockchain technology, 235, 236
  • Cadmium, in fabrication process, 117, 119
  • Capacity,
    • battery, 116
    • current, 116
    • payload, of vehicles, 117
  • Carbon footprint, 115
  • Carbon monoxide, 233
  • Cluster heads (CHs),
    • estimation, 80
    • formation, 81, 82
    • information of, 87
    • lifetime of, 81
    • selection, 81, 82, 84, 86, 87
    • utilization of, 82
  • Clustered WSNs, 84, 85
  • Clustering, 168
  • Clustering approach. see also Artificial intelligence (AI)–based energy-efficient clustering and routing in IoT-assisted WSNs,
    • aggregation mechanisms, 82
    • AI-based, 86–87
    • communication process, 83
    • distributed, 80
    • dynamic, 80
    • EPSO-CEO, 82
    • GAECH, 82
    • GEEC, 83
    • hierarchical-based clustering nodes, 83
    • in IoT applications, 83
    • PSO-HC, 81
    • routing protocols, 81
    • in WSNs, 84–85
  • Conventional statistical, 183
  • Convolutional LSTM (ConvLSTM), 215–216, 219–222
  • Convolutional neural network (CNN), 215–216
  • Correlation, 168
  • Correlation coefficient, 99
  • Coulomb counting, 116
  • Cut-in speed, 152
  • Cut-off speed, 152
  • Cyber attacks, 205
  • Data falsification attacks, microgrid, 206
  • Data falsification detection,
    • machine learning, 206
    • Markov model, 207
  • Dataset preparation, forecasting of electricity consumption, 217–218
  • Deep learning, 129–130, 132, 135, 137, 142
  • Deep learning algorithms, 185
  • Deep neural networks (DNNs), 116, 137, 143, 181
  • Degree-constrained tree (DCT), 81
  • Demand-driven scheme, 235
  • Department of Land Records, 231
  • Desertification and Land Degradation Atlas of India, 230–231
  • Detection, 201–203, 205–207
  • Dew point, 181
  • Digitalization of services, 236
  • Drones, 117
  • DSR protocol, 81
  • Dynamic battery modeling, 119–122
  • Dynamic performance, 12
  • Eigenvalues, 15
  • Electric vehicles, 117
  • Electricity consumption forecasting, for G20. see Forecasting of electricity consumption for G20
  • E-NAM (National Agriculture Market) platform, 236
  • Enerdata organization, 217
  • Energy hole issue, 81
  • Energy-efficient clustering and routing. see also Artificial intelligence (AI)–based energy-efficient clustering and routing in IoT-assisted WSNs,
    • EEDAC-WSN, 83
    • GEEC, 83
    • LEACH protocol, 81, 83
  • Energy-efficient data aggregation scheme for clustered WSN (EEDAC-WSN), 83
  • Enhanced PSO-based clustering energy optimization (EPSO CEO) algorithm, 82
  • Ensemble forecasting, 166
  • Ensemble Kalman filter (EnKF), 37
  • Ensemble models, 137, 139–140, 142, 144
  • Epochs, 188, 191–192
  • Equivalent circuit, 6, 7
  • Ethanol, 232, 235, 236
  • Evolutionary algorithms, 93, 96, 103, 106–110
  • Extreme learning machine, 130, 137–138, 143
  • False data injection, 201–202
  • Feed forward neural networks, 213
  • Feed forward network, 97
  • Feed-forward algorithm, 37
  • Feed-forward back-propagation, 37
  • Firefly algorithm, 40, 51
  • First node die (FND), 82
  • Fish oil methyl ester, 232
  • Fish-catching methods, AI-enabled, 232
  • Fisheries,
    • biodiesel production from, 233
    • PMMSY, 233
    • waste generated from, 232, 233
  • Fisheries, and Aquaculture Infrastructure Development Fund (FIDF), 233
  • Flat WSNs, 84
  • Flipkart, 236
  • Flux linkage per second, 5
  • Forecast groundwater levels, 39
  • Forecasting models, 129, 131–134, 138, 139, 143–144
  • Forecasting of electricity consumption for G20,
    • dataset preparation, 217–218
    • history and advancement, 212–213
    • overview, 211–217
    • performance of all trained models, 219
    • predictions done by models, 221–222
    • recurrent neural networks, 213–216
    • regression techniques,
    • requirement, 212
    • results and discussions, 218–224
    • statistical analysis of prediction done by LSTM model, 223–224
    • training and test size for generated dataset, 218
  • Forget gate, LSTM cell, 214
  • Fuel price regulator, 235
  • Fuzzy adaptive resonance theory mapping network with wavelet transform, 51
  • Fuzzy ART (adaptive resonance theory), 37, 50
  • Fuzzy logic, 185
  • Game theory–based energy-efficient clustering routing protocol (GEEC), 82, 83
  • Gated recurrent unit (GRU), 137, 139, 181, 185, 190, 214–215, 219–222
  • Genetic algorithm, 103–104
  • Genetic algorithm–based energy-efficient clustering hierarchy (GAECH), 82
  • Genetic programming model, 40
  • Geographic information system (GIS) mapping, 231, 235
  • Global hunger index 2020, 234
  • Gradient descent with momentum and adaptation learning rate back-propagation algorithm (GDX), 37
  • Green gas emissions, 116
  • GRU, 181, 185, 190–191, 197
  • Half node die (HND), 82
  • Hidden layer neurons, 45
  • Hidden layers and neurons, 39
  • Hierarchical clustering (HC), PSO protocol for, 81
  • Hierarchical routing protocols, 82–83
  • Hierarchical-based clustering nodes, 83
  • Historical data, 158, 184
  • HPCL, 232
  • Huber as objective function, 220
  • Humidity, 181, 184, 191
  • Humpback whale activity, 39
  • Hybrid method, 158
  • Hybrid model, 183
  • Hydraulic energy, 55
  • Incremental learning, 135–136
  • India,
    • biodiesel, relevance of, 233–234
    • biodiesel extraction in, 235
    • biofuels in commercial flights, 232
    • FIDF, 233
    • IOCL and HPCL, 232
    • ISPRL, 233
    • Jamnagar refinery in Gujarat, 233
    • Kharif, stubble burning of, 232
    • marines and fisheries sector, 232–233
    • National Biofuel Policy 2018, 232
    • new farm bills, 232
    • perspective of renewable biofuels, 230–232
    • PMMSY, 233
    • refining capacity of crude oil, 233
  • Indian Strategic Petroleum Reserves Limited (ISPRL), 233
  • Indigenous Indian Satellites, 231
  • Induced tree of crossed cube (ITCC), 83
  • Information, communication, and technology (ICT), 233
  • Input gate, LSTM cell, 214
  • Intended nationally determined contributions (INDC), 230
  • Internet of things (IoT)–assisted WSNs, 83–84. see also Artificial intelligence (AI)–based energy-efficient clustering and routing in IoT-assisted WSNs
  • Interval forecasting, 133–134, 140
  • Intrusion detection system, 202
  • IOCL, 232
  • ISRO, 230, 231
  • Jamnagar refinery in Gujarat, 233
  • Jatropha Curcas, 231, 232, 235
  • Kalman Filter technique, 116, 119, 121
  • Kharif, stubble burning of, 232
  • Last node die (LND), 82
  • LCO (lithium cobalt oxide) battery, 117
  • LEACH protocol, energy-efficient, 81, 83
  • Lead acid, in fabrication process, 117, 119
  • Levenberg-Marquardt (LM), 42
  • Levenberge-Marquardt algorithm, 174
  • LFP (lithium ion phosphate) battery, 117
  • Linear regression, forecasting of electricity consumption, 216–217, 220, 221
  • Linearization, 10
  • Lithium cobalt oxide (LCO) battery, 117
  • Lithium ion phosphate (LFP) battery, 117
  • Lithium iron manganese phosphate (LiFeMnPO4) battery, 116
  • Lithium, in fabrication process, 117, 119
  • LM algorithm (LMA), 45
  • Long short-term memory (LSTM), 130, 137–138, 143–144, 181, 184, 214–216, 219–224
    • bidirectional LSTM, 216, 219–222
    • ConvLSTM, 215–216, 219–222
  • Long-term drought strategies, 40
  • Long-term forecasting (LTF), 159
  • LSSVM, 185
  • LSTM, 181–182, 184, 186–188, 191, 197
  • Machine learning (ML), forecasting of electricity consumption for G20 members using (see Forecasting of electricity consumption for G20)
  • Machine learning algorithms, 185
  • MAE, 181–182, 197
  • Marine waste, 233
  • Markov Chain Monte Carlo (MCMC) algorithm, 217
  • MATLAB 2015b, 122
  • Mean absolute deviation, 98
  • Mean absolute error, 174–175, 181–182
  • Mean square error, 98
  • Medium-term forecasting (MTF), 159
  • Mercury, in fabrication process, 117, 119
  • Metaheuristic, 51–52, 70
  • Methanogenesis, 94–95
  • Methods of forecasting,
  • MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act), 235
  • Middle Awash Agricultural Development Enterprise (MAADE), 41
  • Minimum support price (MSP), 231
  • Ministry of Fisheries, Animal Husbandry, and Dairying, 232
  • Mission innovation (MI) program, 233
  • Modeling, 4, 38, 40, 42–44, 46–49, 51, 59–60, 63–66
  • Modeling, SoC. see State of charge (SoC) modeling
  • MSE, 182, 196–197
  • Multi-hop routing protocols, 82
  • Multilayer perceptron (MLP), 52
  • Multi-objective forecasting, 133, 134
  • Multiphase machine, 3
  • Multiple-sink placement algorithm, 81
  • Multi-step forecasting, 133–134, 142, 144
  • Multi-task learning, 130, 135, 137, 142
  • Mutual coupling, 6
  • National Biofuel Policy, 232, 234
  • Natural language processing, and forecasting, 213
  • Neural networks (NNs), 52, 116, 183, 184
  • Neural networks, RNNs, 213–216
  • Neural structural learning, 136–137
  • Nickel metal hydride, in fabrication process, 117, 119
  • Nitrogen oxide (NOx) emissions, 233
  • Nonlinear autoregressive exogenous (NARX), 37, 53
  • Non-probabilistic algorithms, 85
  • Normalization method, 48
  • On-hole alert (OHA) protocol, 81
  • On-hole children reconnection (OHCR) protocol, 81
  • Open-circuit voltage (OCV), 117, 119, 125
  • Optimization, 38–39, 48, 51, 55–57, 60, 62–63, 70, 73–76
  • Output gate, LSTM cell, 214
  • Packet drop, 205
  • Parametric variation,
    • damper winding, 22, 24
    • field circuit, 19
    • magnetizing reactance, 26
    • stator, 16
  • Paris Agreement, 230
  • Particle swarm optimization, 103, 106–107
  • Peak humidity, 45
  • Perovskite solar cells, 116
  • Persistence method, 158, 159
  • Persistence model, 133
  • Photovoltaic (PV) arrays, solar,
  • Physical, 183
  • Physical attacks, 205
  • Physical method, 158
    • advanced power curve models, 158
    • numerical weather prognostication (NWP), 158
    • statistical downscaling, 158
  • Polynomial regression,
  • Power coefficient, 150
  • Pradhan Mantri Matsya Sampada
  • Yojna (PMMSY), 233
  • Prediction, 38–40, 42–45, 47–48, 51–55, 58–60, 62, 64, 66–68, 70–72
  • Prediction framework, 155
  • Pressure, 181, 191
  • Probabilistic algorithms, 85
  • Probabilistic forecasting, 133, 138, 144
  • Process automation, AI in, 231
  • PSO protocol for hierarchical clustering (PSO-HC), 81
  • Python, 218
  • Quality-of-service (QoS)–based routing protocols, 81
  • Radial basis function neural network (RBFN), 37, 39
  • Ramp forecasting, 133, 134
  • Recurrent neural networks (RNNs), 213–216
  • Regression techniques, forecasting of electricity consumption,
  • Regression, polynomial,
  • Reinforcement learning (RL) approach, 116
  • ReLU as activation function, 220
  • Renewable biofuels, Indian perspective of, 230–232
  • Renewable energy, 38, 62, 148–149
  • Renewable energy source (RES), 230
  • Reset gate, GRU, 214
  • Ring routing protocols, 82
  • RMSE, 182, 196–197
  • RNNs. see Recurrent neural networks (RNNs)
  • Root mean square error, 99, 182
  • Routing, in IoT–assisted WSNs. see also Artificial intelligence (AI)–based energy-efficient clustering and routing in IoT–assisted WSNs,
    • AI-based energy-aware routing protocol, 87–88
    • hierarchical routing protocols, 82–83
    • problem, 81
    • QoS–based routing protocols, 81
    • ring routing protocols, 82
    • routing protocols, security, 84
  • Ryzen 5 4600H, 218
  • Scene learning, 135–136, 143
  • ScikitLearn, 218
  • Sequential learning, 135–136
  • Shark smell optimization (SSO), 37, 51
  • Shortest-path tree (SPT), 81
  • Short-term forecasting (STF), 159
  • Sink nodes, 80–82, 84
  • Six-phase synchronous machine, 4
  • Smart grid, 201–205, 206–208
  • Smart meter, 201–205, 207–208
  • SoC (state of charge) modeling. see State of charge (SoC) modeling Solar irradiation, 49, 51, 68–70
  • Solar panels, thin-film crystalline–based, 117
  • Solar PV arrays,
  • Solar radiation, 47–48, 51, 60, 66–70
  • Solar-based PV cells, 116–117
  • Space satellites, 117
  • Spatial correlation, 183
  • Speech recognition, 213
  • Stability analysis, 15
  • State of charge (SoC) modeling,
  • Statistical method, 158
  • Stochastic gradient descent (SGD), 169
  • Support vector machine (SVM), 165
    • hyperplane, 165
    • soft margin, 165
    • SVM kernels, 165
  • Support vector machine (SVM) approach, 116
  • Support vector regression (SVR), forecasting of electricity
  • Sustainable energy, 230, 233, 237
  • Sustainable energy sources, 115
  • SVM, 185–186
  • Taylor series expansion, 10
  • Temperature, 181, 184, 191
  • TensorFlow, 218
  • Theil’s coefficient, 217
  • Three-layer back-propagation network, 41
  • Time series method, 158–159
    • non-stationary time series, 159
    • stationary time series, 159
  • TORA protocol, 81
  • Total geographical area (TGA), 231
  • Transfer learning, 131, 135–136, 143
  • Unemployment, urban, 235
  • Update gate, GRU, 214
  • Urban employment guarantee scheme (UEGS), 235
  • Utilization and planning, 43
  • Vapnik-Chervonenkis control model, 217
  • Variation in load, 28
  • Very short-term forecasting (VSTF), 159
  • Viterbi algorithm, 207
  • Voltage equations, 5
  • Voronoi cell, 83
  • Wasteland Atlas of India, 231
  • Wavelet neural network (WNN), 37
  • Wavelet soft threshold denoising (WSTD), 168
  • Weakly connected dominating set (WCDS), 82
  • WEMER protocol, 83
  • Whale optimization algorithm (WOA), 37, 39
  • Wind direction, 184
  • Wind energy, 149–150
  • Wind farms, 182
  • Wind forecasting,
    • long duration, 153
    • short duration, 153
  • Wind forecasting models,
    • hybrid method, 155
    • physical method, 154
    • statistical method, 154
  • Wind generation, 184
  • Wind power, 184
  • Wind prediction, 184
  • Wind speed, 181–182, 184, 186, 191
  • Wind turbines, 182–184
  • Wireless sensor networks (WSNs), IoT-assisted,
    • ad hoc topology used by, 84
    • AI–based energy-efficient
    • clustering in, 84–85
    • creating, 85–86
    • EEDAC-WSN, 83
    • energy efficiency in, 83
    • energy-efficient routing process in, 86–87
    • flat and clustered networks, 84, 85
    • IoT application, 83
    • sensor nodes, 83–84
  • Wireless sensor node, 83–84
  • World Trade Organization (WTO), 234
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