Intra-Day Solar Irradiance Forecasting for Remote Microgrids Using Hidden Markov Model

Accurate solar irradiance forecasting is the key to accurate estimation of solar power output at any given time. The accuracy of this information is especially crucial in diesel-PV based remote microgrids with batteries to determine the set points of the batteries and generators for their optimal dispatch. This, in turn, is related directly to the overall operating cost because both an overestimation and an underestimation of the irradiance means additional operating costs for either suddenly ramping up the backup resources or causing under-utilization of the available PV power output. Accurately predicting the solar irradiance is not an easy task because of the sporadic nature of the irradiance that is received at the solar panel surfaces. Handling the dynamic nature of the irradiance pattern requires a strong and flexible model that can precisely capture the irradiance trend in any given location at a given time. Usually, such a robust model requires a lot of input variables like weather data including humidity, temperature, pressure, wind speed, wind direction, etc. and/or large inventory of satellite images of clouds over a long period of time. The expensive sensors and database tools for collecting and storing such huge information may not be installed in remote locations. Therefore, this thesis prioritizes on developing a simple method requiring a minimum input to accurately forecast the solar irradiance for remote microgrids

Data Center Load Forecast using Hidden Markov Models

The energy cost of data centers tantamount to their overall operational cost. A possible solution to this immense cost could be proper scheduling of the power resources. This can be achieved by forecasting the data center loads. However, highly variable nature of the data center loads makes it challenging to use the traditional methods of load forecasting. In this paper, a stochastic method based on Hidden Markov process is developed to model the data center load and is used for a day-ahead forecasting. This method is out-standing because of its flexibility in addressing the variable nature of the data center load. The utility of the model is illustrated using a dataset from National Renewable Energy Laboratory - Research Support Facility (NREL - RSF). Two models created based on the proposed method yielded Mean Absolute Percentage Errors (MAPE) of 1.49% and 3.89%