Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

Collinsville solar thermal project: yield forecasting (draft report)

The final report has been published and is available here. Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  The subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projects but its three nearby neighbours do possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified TMY technique to overcome technology specific Typical Metrological Year (TMY).  We discuss the effect of climate change and the El Nino Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research question arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Is BoM’s DNI satellite dataset adequately adjusted for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO affect yield? Given the 2007-2012 constraint, will the TMY process provide a “Typical” year over the ENSO cycle? How does climate change affect yield? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield. 4        Results and analysis In the results section we present the four driver models selected and the process that was undertaken to arrive at the models. 5        Discussion We analyse the extent to which the research questions are informed by the results. 6        Conclusion In this report, we have identified the key research questions and established a methodology to address these questions.  The models for the four drivers have been established allowing the calculation of the yield projections for Collinsville

Collinsville solar thermal project: yield forecasting (final report)

Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  Our subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projections but its three nearby neighbours possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified Typical Metrological Year (TMY) technique to overcome technology specific TMYs.  We discuss the effect of climate change and the El Niño Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research questions arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Does BoM adequately adjust its DNI satellite dataset for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO cycle affect yield? Given the 2007-12 electricity demand data constraint, will the 2007-13 based TMY provide a “Typical” year over the ENSO cycle? How does climate change affect yield? Is the method to use raw satellite DNI data to calculate yield and retrospectively adjusting the calculated yield with an effective to satellite DNI energy per area ratio suitable? How has climate change affected the ENSO cycle? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield.  We also discuss the development of the technology specific TMY and sensitivity analysis to address the research questions on climate change and elevation. 4        Results and analysis In the results section we present the selection process for the four driver models.  We also present the effective to satellite DNI ratio, the annual variation in gross yield, the selection of TMMs for the TMY based on monthly yield, the sensitivity analysis results on climate change and elevation, and the frequency of gross yield exceeding 30 MW. 5        Discussion We analyse the results within a wider context, in particular, we make a comparison with the yield calculations for Rockhampton to address the overarching research question.  We find that the modelling of weather at Collinsville using incomplete weather data has higher predictive performance that using the complete weather data at Rockhampton but recommend using the BoM’s one-minute solar data to improve the comparative test.  Other findings include the requirement to increase the current TMM’s selection period 2007-13 to incorporate more of the ENSO cycle.  There is less than 0.3% change in gross yield from the plant in the most likely case of climate change but there is a requirement to determine the effect of climate change on electricity demand and the ensuing change in wholesale electricity prices. 6        Conclusion In this report, we have addressed the key research questions, produced the yield projections for our subsequent report ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) and made recommendations for further research

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

Selecting a suitable energy demand forecasting method is challenging due to the complex interplay of long-term trends, short-term seasonalities, and uncertainties. This paper compares four time-series models performance to predict total and peak monthly energy demand in India. Indian's Central Energy Authority's (CEA) existing trend-based model is used as a baseline against (i) Seasonal Auto-Regressive Integrated Moving Average (SARIMA), (ii) Long Short Term Memory Recurrent Neural Network (LSTM RNN) and (iii) Facebook (Fb) Prophet models. Using 108 months of training data to predict 24 months of unseen data, the CEA model performs well in predicting monthly total energy demand with low root-mean square error (RMSE 4.23 GWh) and mean absolute percentage error (MAPE, 3.4%), but significantly under predicts monthly peak energy demand (RMSE 13.31 GW, MAPE 7.2%). In contrast, Fb Prophet performs well for monthly total (RMSE 4.23 GWh, MAPE 3.3%) and peak demand (RMSE 6.51 GW, MAPE 3.01%). SARIMA and LSTM RNN have higher prediction errors than CEA and Fb Prophet. Thus, Fb Prophet is selected to develop future energy forecasts from 2019 to 2024, suggesting that India's annual total and peak energy demand will likely increase at an annual growth rate of 3.9% and 4.5%, respectively.</p

A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India

Selecting a suitable energy demand forecasting method is challenging due to the complex interplay of long-term trends, short-term seasonalities, and uncertainties. This paper compares four time-series models performance to predict total and peak monthly energy demand in India. Indian's Central Energy Authority's (CEA) existing trend-based model is used as a baseline against (i) Seasonal Auto-Regressive Integrated Moving Average (SARIMA), (ii) Long Short Term Memory Recurrent Neural Network (LSTM RNN) and (iii) Facebook (Fb) Prophet models. Using 108 months of training data to predict 24 months of unseen data, the CEA model performs well in predicting monthly total energy demand with low root-mean square error (RMSE 4.23 GWh) and mean absolute percentage error (MAPE, 3.4%), but significantly under predicts monthly peak energy demand (RMSE 13.31 GW, MAPE 7.2%). In contrast, Fb Prophet performs well for monthly total (RMSE 4.23 GWh, MAPE 3.3%) and peak demand (RMSE 6.51 GW, MAPE 3.01%). SARIMA and LSTM RNN have higher prediction errors than CEA and Fb Prophet. Thus, Fb Prophet is selected to develop future energy forecasts from 2019 to 2024, suggesting that India's annual total and peak energy demand will likely increase at an annual growth rate of 3.9% and 4.5%, respectively.</p

Hybrid Grey Forecasting Model for Iran's Energy Consumption and Supply

Grey theory deals with systems that are characterized by poor information or for which information is lacking. This study presents an improved grey GM (1, 1) model, using a technique that combines residual modification with Markov Chain model. We use energy consumption and supply of Iran to test the accuracy of proposed model. The results show that the Markov Chain residual modification model achieves reliable and precise results.  Keywords: Grey Forecasting Model; Markov Chain; Energy System JEL Classifications: C15; C53; C6

Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The electricity consumption forecasting problem is especially important for policy making in developing region. To properly formulate policies, it is necessary to have reliable forecasts. Electricity consumption forecasting is influenced by some factors, such as economic, population and so on. Considering all factors is a difficult task since it requires much detailed study in which many factors significantly influence on electricity forecasting whereas too many data are unavailable. Grey convex relational analysis is used to describe the relationship between the electricity consumption and its related factors. A novel multi-variable grey forecasting model which considered the total population is developed to forecast the electricity consumption in Shandong Province. The GMC(1,N) model with fractional order accumulation is optimized by changing the order number and the effectiveness of the first pair of original data by the model is proven. The results of practical numerical examples demonstrate that the model provides remarkable prediction performances compared with the traditional grey forecasting model. The forecasted results showed that the increase of electricity consumption will speed up in Shandong Province