• Energy Research

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.

The short-term power load prediction of single households is a challenging issue in the research fields of Smart Grid (SG) management/planning, viable energy usage, energy saving and the bidding system design of electricity market. The reason for this is the unpredictability and uncertainty in electricity consumption pattern of individual household. The energy management/planning of the SGs is even becoming more complex due to the integration of Distributed Energy Resources (DERs). The DERs are useful in decreasing the bill of the electricity consumer by empowering them to produce their own green energy. With the huge development in Advanced Metering Infrastructure (AMI), Big Data (BD) and machine learning models, the potential benefits of dynamic pricing schemes and DERs can be fully accomplished. But, the accurate prediction of power generated through DERs as well as forecasting the user power profile is a big issue. The user power profile varies hourly, daily, weekly and seasonally due to the various environmental and seasonal effects. In this work, the focus is on exploring and evaluating machine learning models for accurately predicting user power profile for energy management in a smart community. Eight regression models are evaluated for the prediction of the power consumption of the single household. The simulation results indicate that the Radial Basis Function (RBF) kernel is the most suitable machine learning model for forecasting the short term power consumption of the single household.

Agricultural practices in Africa uplift the economy and sustain livelihoods. However, in recent years numerous issues like climate change, reduced productivity, narrowing resources, and increased prevalence have emerged. In order to combat these obstacles, we devised a plan that integrates IoT, AI ML, and geospatial technology. By analyzing 45 industrial reports and peer-reviewed journals we found out that the use of advanced technology makes resource optimization, irrigation management, and disease detection significantly easier. Our findings reveal some astonishing facts like the efficiency of CNN neural networks in terms of disease detection which stood at an impressive 92 %, then there are neural networks that achieve an accuracy of 88.9 % in predicting the yield of crops. Lastly, RL has managed to attain a water-saving efficiency of an impressive 25.4 %. Despite these advancements, the adoption rate in Africa is still low, this can be attributed to poor infrastructure, lack of funds, and absence of professional knowledge. In order to counter these shortcomings, we suggest political term initiatives, initiatives aimed at enhancing expertise and knowledge, and affordable IOT implementation. In addition to identifying the socio-economic and infrastructural barriers to technology adoption, this essay also offers suggestions that advocate for facilitating sustainable agricultural practices in Africa. So that the identified gaps are bridged, the research aids in enhancing climate change resilience for sustainable growth in the agriculture industry of the continent.

In this research work, we investigated a dual switch (DS) active rectifier for the piezoelectric (PE) energy scavenging system. In the proposed DS active rectifier configuration, two extra switches are shunted across the PE transducer which helps the PE transducer’s capacitor in charging and discharging which results in maximum power extraction from the PE transducer. Moreover, in the proposed rectifier configuration comparator controlled active diodes are used instead of conventional/passive diodes to minimize the threshold voltage V T H drop. The proposed DS active rectifier design is fabricated in a 1-poly 6-metal 180-nm standard CMOS process. The simulation and measured results of the proposed DS active rectifier design have the better power conversion efficiency (PCE) of 91.5 %, which definitely helps in extracting more power than the conventional full bridge rectifier (FBR).

In this paper, a fast and accurate hybrid electric load forecasting (FA-HELF) framework is proposed. The proposed FA-HELF is an integrated framework of three modules. First, random forest and relief-F algorithms are fused together to propose a hybrid feature selection technique for the purpose to eliminate redundancy. Second, the kernelbased principle component analysis is introduced for feature extraction in order to overcome the problem of dimensionality reduction. Finally, to perform fast and accurate load forecasting heuristic based optimizer is integrated with a support vector machine (SVM) forecaster. The proposed FA-HELF framework shows significant improvements than other existing forecasting models in terms of forecast accuracy and convergence rate.

With rapid advancements in renewable energy sources, billing mechanism (AMI), and latest communication technologies, the traditional control networks are evolving towards wise, versatile and collaborative Smart Grids (SG). The short term power load forecasting of individual as well as group of similar energy customers is critical for effective operation and management of SG. Forecasting power load of individual as well as group of similar energy customers is challenging compared to aggregate load forecasting of a residential community. The main reason is the high volatility and uncertainty involved for the case of individual and group of similar energy customers. Several machine/deep learning models have been developed in the recent past for forecasting load of individual energy customers, but such explorations are ineffective due to the requirement of one trained model for every energy customer, which is practically not feasible. We plan to build a deep learning model using convolutional neural network (CNN) layers in pyramidal architecture for effective load forecasting for a group of similar energy-profile customers. Initially, we grouped a subset of energy customers from database of Smart Grid Smart City (SGSC) into clusters using DBSCAN approach. The CNN layers are used for extracting feature from historical load of each cluster. The extracted feature of similar energy-profile customers (grouped based on clustering) is combined to make training-databases for each cluster. We have used the power load data from SGSC project, which contain thousands of individual household energy customers data. The developed Pyramid-CNN model is trained based on these sets of databases. The trained model is evaluated on randomly selected customers from few clusters. We obtained significantly improved forecasting results for randomly selected user from different clusters. Our adapted strategy of clustering based model training resulted in upto 10 percent MAPE improvement for the energy customers. The essence of our work is that energy customers can be grouped into clusters and then representative model could be developed/trained, which can accurately forecast power load for individual energy-customer. This approach is highly feasible, as we do not need to train a model per energy customer and still achieve competitive forecasting results.

Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and off-peak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. As a result, a fairer system is realized, and the total revenue of the utility company is assured.

In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques.