Short-term global horizontal irradiance forecasting using weather classified categorical boosting

Accurate short-term solar irradiance (SI) forecasting is crucial for renewable energy integration to ensure unit commitment and economic load dispatch. However, hourly SI prediction is very challenging due to atmospheric conditions and weather fluctuations. This study proposes a hybrid approach using weather classification and boosting algorithms for short-term global horizontal irradiance (GHI) forecasting. In data pre-processing steps, we employ random forest for feature selection and K-means clustering for weather classification. The weather-based clustered data is used for the model training using categorical boosting (CatBoost). The proposed weather-classified categorical boosting (WC-CB) scheme is compared with benchmarks in literature like adaptive boosting (AdaBoost), bi-directional long short-term memory (BiLSTM) and gated recurrent unit (GRU) using datasets from two distinct geographical locations obtained from the National Solar Radiation Database (NSRDB). The results show that the proposed WC-CB hybrid approach has lower forecast errors compared to conventional CatBoost modelling. The error reduction of 16% and 39% in root mean square error and 6% and 9% in mean absolute error is recorded for the two datasets, respectively. These findings demonstrate the importance of weather classification in improving forecasting accuracy with potential implications for broader renewable energy applications

A hybrid approach for forecasting occupancy of building’s multiple space types

The occupancy datasets are useful for planning important buildings’ related tasks such as optimal design, space utilization, energy management, maintenance, etc. Researchers are currently working on two key issues in building management systems. First, feasible and economical deployment of indoor and outdoor weather and energy monitoring sensors for data acquisition. Second, the development and implementation of cost-effective data-driven models with regular monitoring to ensure satisfactory performance for occupancy prediction. In this context, we present an occupancy forecasting model for different types of rooms in an academic building. A comprehensive dataset comprising indoor and outdoor environmental variables such as energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) operational details and information on Wi-Fi-connected devices of a campus building, is used for occupants’ count prediction. A Light Gradient Boost Machine (LGBM) is applied for the selection of suitable features. After the feature selection, Machine Learning (ML) models such as Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), Long Short-Term Memory (LSTM) and Categorical Boosting (CatBoost) are employed to predict occupants’ count in each room. The models’ performances are evaluated using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Normalized Root Mean Square Error (NRMSE). The proposed LGBM-XgBoost model outperforms other approaches for each type of space. Moreover, to highlight the importance of LGBM as a feature selection technique, the XgBoost model is also trained with all features. Results indicate that by selecting the appropriate features through LGBM, the RMSE and MAE for lecture rooms 1 and 2 are improved by 61.67%, 36.17% and 67.05%, 63.67%, respectively. Similarly, for office rooms 1 and 2 RMSE and MAE are improved by 33.37%, 71.5% and 59.7%, 51.45%, respectively

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model

A Novel Prosumer-Based Energy Sharing and Management (PESM) Approach for Cooperative Demand Side Management (DSM) in Smart Grid

Increasing population and modern lifestyle have raised energy demands globally. Demand Side Management (DSM) is one important tool used to manage energy demands. It employs an advanced power infrastructure along with bi-directional information flow among utilities and users in order to achieve a balanced load curve and minimize demand-supply mismatch. Traditionally, this involves shifting the electricity demand from peak hours to other times of the day in an optimized manner. Multiple users equipped with renewable resources work in coordination with each other in order to achieve mutually beneficial energy management. This, in turn, has generated the concept of cooperative DSM. Such users, called prosumers, consume and produce energy using renewable resources (solar, wind etc.). Prosumers with surplus energy sell to the grid as well as to other consumers. In this paper, a novel Prosumer-based Energy Sharing and Management (PESM) scheme for cooperative DSM has been proposed. A simulation model has been developed for testing the proposed method. Different variations of the proposed methodology have been experimented with different criteria. The results show that the proposed energy sharing scheme achieves DSM purposes in a useful manner

IoT applications and challenges in smart cities and services

Abstract Internet of Things (IoT) is a revolutionary and novel platform where a smart network connects to the large number of electronic devices via internet through available communication systems for reliable and real time connectivity, sensing thus acquiring data from sensors, computing and actuating devices. A review of the current status of IoT features, architecture, communication infrastructure and applications is presented here. Number of IoT applications and challenges in the development of smart cities are covered about this innovative technological platform in this review along with communication protocols characteristics and applications. IoT aided smart city services, health applications, transport applications and smart grid improvements are also discussed. Many challenges and issues related to security, communication, applications and system architecture along with future directions of IoT technology are elaborated in detail

Efficient optimization techniques for resource allocation in UAVs mission framework.

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of 'n' candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid

Efficient optimization techniques for resource allocation in UAVs mission framework

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of ‘n’ candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid

An Enhanced System Architecture for Optimized Demand Side Management in Smart Grid

Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce peak load and, consequently, the Peak to Average Ratio (PAR). This paper presents a Comprehensive Home Energy Management Architecture (CHEMA) with integration of multiple appliance scheduling options and enhanced load categorization in a smart grid environment. The CHEMA model consists of six layers and has been modeled in Simulink with an embedded MATLAB code. A single Knapsack optimization technique is used for scheduling and four different cases of cost reduction are modeled at the second layer of CHEMA. Fault identification and electricity theft control have also been added in CHEMA. Furthermore, carbon footprint calculations have been incorporated in order to make the users aware of environmental concerns. Simulation results prove the effectiveness of the proposed model

A two-stage multi-agent EV charging coordination scheme for maximizing the grid performance and customer satisfaction

No abstract available