Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP with ARIMA Models

Short-term electricity price forecasting has become important for demand side management and power generation scheduling. Especially as the electricity market becomes more competitive, a more accurate price prediction than the day-ahead locational marginal price (DALMP) published by the independent system operator (ISO) will benefit participants in the market by increasing profit or improving load demand scheduling. Hence, the main idea of this paper is to use autoregressive integrated moving average (ARIMA) models to obtain a better LMP prediction than the DALMP by utilizing the published DALMP, historical real-time LMP (RTLMP) and other useful information. First, a set of seasonal ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed and compared with autoregressive moving average (ARMA) models that use the differences between DALMP and RTLMP on their forecasting capability. A generalized autoregressive conditional heteroskedasticity (GARCH) model is implemented to further improve the forecasting by accounting for the price volatility. The models are trained and evaluated using real market data in the Midcontinent Independent System Operator (MISO) region. The evaluation results indicate that the ARMAX-GARCH model, where an exogenous time series indicates weekend days, improves the short-term electricity price prediction accuracy and outperforms the other proposed ARIMA modelsComment: IEEE PES 2017 General Meeting, Chicago, I

Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase(PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.Comment: ACL 201

A Strategic Approach to Agricultural Research Program Planning in Sub-Saharan Africa

Recent studies have shown that agricultural research can have high payoffs in Africa, but impact depends on how well technology fits with evolving needs and capacity in the agricultural sector and the rest of the economy. Structural adjustment policies (e.g., market liberalization, currency devaluation) and political change are transforming user demands for new technology and the economic environment in which technology must perform. The challenge is how to design agricultural research as a strategic input to promote broad-based economic growth, structural transformation, and food security in the increasingly market-driven, but fragile, economies of Africa.Food Security, Food Policy, Agricultural Research, Research and Development/Tech Change/Emerging Technologies, Downloads May 2008-July 2009: 44, Q18,

Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets

One emerging area of agent-based modelling is retail markets; however, there are problems with modelling such systems. The vast size of such markets makes individual-level modelling, for example of customers, difficult and this is particularly true where the markets are spatially complex. There is an emerging recognition that the power of agent-based systems is enhanced when integrated with other AI-based and conventional approaches. The resulting hybrid models are powerful tools that combine the flexibility of the agent-based methodology with the strengths of more traditional modelling. Such combinations allow us to consider agent-based modelling of such large-scale and complex retail markets. In particular, this paper examines the application of a hybrid agent-based model to a retail petrol market. An agent model was constructed and experiments were conducted to determine whether the trends and patterns of the retail petrol market could be replicated. Consumer behaviour was incorporated by the inclusion of a spatial interaction (SI) model and a network component. The model is shown to reproduce the spatial patterns seen in the real market, as well as well known behaviours of the market such as the "rocket and feathers" effect. In addition the model was successful at predicting the long term profitability of individual retailers. The results show that agent-based modelling has the ability to improve on existing approaches to modelling retail markets.Agents, Spatial Interaction Model, Retail Markets, Networks

Combining human and machine intelligence for making predictions

Thesis (S.M. in Management Research)--Massachusetts Institute of Technology, Sloan School of Management, June 2013."June 2012." Cataloged from PDF version of thesis.Includes bibliographical references (p. 28-32).An extensive literature in psychology, economics, statistics, operations research and management science has dealt with comparing forecasts based on human-expert judgment vs. (statistical) models in a variety of scenarios, mostly finding advantage of the latter, yet acknowledging the necessity of the former. Although computers can use vast amounts of data to make predictions that are often more accurate than those by human experts, humans are still more adept at processing unstructured information and at recognizing unusual circumstances and their consequences. Can we combine predictions from humans and machines to get predictions that are better than either could do alone? We used prediction markets to combine predictions from groups of people and artificial intelligence agents. We found that the combined predictions were both more accurate and more robust in comparison to those made by groups of only people, or only machines. This combined approach may be especially useful in situations where patterns are difficult to discern, where data are difficult to codify, or where sudden changes occur unexpectedly.by Yiftach Nagar.S.M.in Management Researc

Do Callable Convertibles Support The Investment Process Of A Company? An Analysis Of The World Market Of Hybrid Debt

Using a sample of 1,705 convertible bonds issued by manufacturing and service companies from the United States (1,138 issues); Europe (270); and Asia (297) between 2004 and 2014 this paper investigates the role of callable convertibles in the corporate investment process. This research shows first that callable convertibles are used to finance investment projects particularly by American firms which may exercise new investment options to improve poor financial performance. Secondly, the same strategy may be followed by European companies, but they seem not to carry out investments on as large a scale as American firms. Thirdly, the research results do not provide evidence that Asian enterprises use callable convertibles for investment purposes: they likely use these instruments for different reasons