The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mix-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.

Grid integration of Solar energy positively affects energy market due to inexhaustible fuel supply and virtually zero emissions. However, inexhaustible renewable fuel supply is punctuated by the problem of intermittency. Intermittency exacerbates the problem of grid operators to bridge the supply and demand gap. Thus, precise output power forecast of grid interfaced Photovoltaic (PV) systems is required for economic dispatch, market regulation, and stable grid operation. This study compares the statistical models of Gaussian Process Regression (GPR) and Support Vector Machine (SVM) for solar power prediction. The models are trained to predict PV system output power against the backdrop of data recorded for Abbottabad City, Pakistan. Both the models have been trained, validated, and compared with each other for varying irradiance and temperature settings. The results depicted that SVM based modeling excel in solar power prediction with Root Mean Square Error (RMSE) lower than GPR based modeling technique. Performance evaluation of models is conducted with error metrics of RMSE, Mean Absolute Error (MAE), and Mean Square Error (MSE). Moreover, prediction quality is qualified based on residual analysis benchmarked by load line analysis of PV system in Simulink.