Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics

Short-term hydro-meteorological forecasting with extreme learning machines

In machine learning (ML), the extreme learning machine (ELM), a feedforward neural network model which assigns random weights in the single hidden layer and optimizes only the weights in the output layer, has the fully nonlinear modelling capability of the traditional artificial neural network (ANN) model but is solved via linear least squares, as in multiple linear regression (MLR). Chapter 2 evaluated ELM against MLR and three nonlinear ML methods (ANN, support vector regression and random forest) on nine environmental regression problems. ELM was then developed for short-term forecasting of hydro-meteorological variables. In situations where new data arrive continually, the need to make frequent model updates often renders ANN impractical. An online learning algorithm – the online sequential extreme learning machine (OSELM) – is automatically updated inexpensively as new data arrive. In Chapter 3, OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1–3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates, with the nonlinear OSELM easily outperforming the benchmark, the online sequential MLR (OSMLR). A major limitation of OSELM is that the number of hidden nodes (HN), which controls the model complexity, remains the same as in the initial model, even when the arrival of new data renders the fixed number of HN sub-optimal. A new variable complexity online sequential extreme learning machine (VC-OSELM), proposed in Chapter 4, automatically adds or removes HN as online learning proceeds, so the model complexity self-adapts to the new data. For streamflow predictions at lead time of one day, VC-OSELM outperformed OSELM when the initial number of HN turned out to be smaller or larger than optimal. In summary, by using linear least squares instead of nonlinear optimization, ELM offers a major advantage over a traditional method like ANN. In situations where new data arrive continually, OSELM and VC-OSELM were shown in this thesis to be more useful than ANN and OSMLR.Science, Faculty ofEarth, Ocean and Atmospheric Sciences, Department ofGraduat

Forecasting Methods for Photovoltaic Energy in the Scenario of Battery Energy Storage Systems: A Comprehensive Review

The worldwide appeal has increased for the development of new technologies that allow the use of green energy. In this category, photovoltaic energy (PV) stands out, especially with regard to the presentation of forecasting methods of solar irradiance or solar power from photovoltaic generators. The development of battery energy storage systems (BESSs) has been investigated to overcome difficulties in electric grid operation, such as using energy in the peaks of load or economic dispatch. These technologies are often applied in the sense that solar irradiance is used to charge the battery. We present a review of solar forecasting methods used together with a PV-BESS. Despite the hundreds of papers investigating solar irradiation forecasting, only a few present discussions on its use on the PV-BESS set. Therefore, we evaluated 49 papers from scientific databases published over the last six years. We performed a quantitative analysis and reported important aspects found in the papers, such as the error metrics addressed, granularity, and where the data are obtained from. We also describe applications of the BESS, present a critical analysis of the current perspectives, and point out promising future research directions on forecasting approaches in conjunction with PV-BESS