document: numerous techniques for forecasting electric energy consumption have been proposed in the last few decades . for operators , energy consumption ( load ) 
   forecast is useful in effectively managing power systems . 
   consumers can also benefit from the forecasted information in order to yield maximum satisfaction . 
   in addition to these economic reasons , load forecasting has also been used for system security purposes . when deployed to handle system security problems 
   , it provides expedient information for detecting vulnerabilities in advance . 
   forecasting energy consumed within a particular geographical area greatly depends on several factors , such as , historical load , mean atmospheric temperature , mean relative humidity , population , gdp per capita . over the years 
   , there has been rapid growth annually of about 10% from year 1999 to 2005 for energy demand in the gaza strip . with about 75% of energy demands from service and household sectors , 
   these demands are barely met @xcite . in order to meet these demands and 
   efficiently utilize the limited energy , it is imperative to observe historic trends and make futuristic plans based on past data .    in the past , 
   computationally easier approaches like regression and interpolation , have been used , however , this methods may not give sufficiently accurate results . 
   as advances in technology and sophisticated tools are made , complex algorithmic approaches are introduced and more accuracy at the expense of heavy computational burden can be observed . 
   several algorithms have been proposed by several researchers to tackle electric energy consumption forecasting problem . 
   previous works can be grouped into three@xcite :    _ * time series approach : * _ : :    in this approach , the trend for electric energy consumption is handled    as a time series signal . 
   future consumption is usually predicted based    on various time series analysis techniques . 
   however , time series    approach is characterized with prediction inaccuracies of prediction    and numerical instability . 
   this inaccurate results is due to the fact    the approach does not utilize weather information . 
   studies have shown    that there is a strong correlation between the behavior of energy    consumed and weather variables . 
   zhou r. _ et al_. @xcite proposed a    data driven modeling method using time series analysis to predict    energy consumed within a building . the model in @xcite 
   was applied on    two commercial building and is limited to energy prediction within a    building . 
   basu k. _ et al_. @xcite also used the time series approach    to predict appliance usage in a building for just an hour . 
   +    simmhan y. _ et al_. @xcite used an incremental time series clustering    approach to predict energy consumption . 
   this method in @xcite was    able to minimize the prediction error , however , very large number of    data points were required . 
   autoregressive integrated moving average    ( arima ) is a vastly used time series approach . 
   arima model was used by    chen j. _ et al_. @xcite to predict energy consumption in jiangsu    province in china based on data collected from year 1985 to 2007 . 
   the    model @xcite was able to accurately predict the energy consumption ,    however it was limited to that environment . 
   the previous works on time    series usually use computationally complex matrix - oriented adaptive    algorithms which , in most scenarios , may become unstable . _ * functional based approach : * _ : :    here , a functional relationship between a load dependent variable    ( usually weather ) and the system load is modelled . 
   future load is then    predicted by inserting the predicted weather information into the    pre - defined functional relationship . 
   most regression methods use    functional relationships between weather variables and up - to - date load    demands . 
   linear representations are used as forecasting functions in    conventional regression methods and this method finds an appropriate    functional relationship between selected weather variables and load    demand . 
   liu d. _ et al_. @xcite proposed a support vector regression    with radial basis function to predict energy consumption in a    building . 
   the approach in @xcite was only able to forecast the energy    consumed due to lighting for some few hours . 
   +    in @xcite , a grey model , multiple regression model and a hybrid of    both were used to forecast energy consumption in zhejiang province of    china . 
   yi w. _ et al_. @xcite proposed an ls - svm regression model to    also forecast energy consumption . 
   however , these models were limited    to a specific geographic area . _ 
   * soft computing based approach : * _ : :    this is a more intelligent approach that is extensively being used for    demand side management . 
   it includes techniques such as fuzzy logic ,    genetic algorithm and artificial neural networks ( ann ) ( * ? ? ? 
   * ; * ? ? ? 
   * ;    * ? ? 
   ? * ; * ? ? ? 
   * ; * ? ? ? 
   the ann approach is based on examining the    relationship that exist between input and output variables . 
   ann    approach was used in @xcite to forecast regional load in taiwan . 
   empirical data was used to effectively develop an ann model which was    able to predict the regional peak load . 
   catalo j. p. s. _ et al_.    @xcite used the ann approach to forecast short - term electricity    prices . 
   levenberg - marquardt s algorithm was used to train data and the    resulting model @xcite was able to accurately forecast electricity    prices . 
   however , it was only able to predict electricity prices for    about 168 hours . 
   +    pinto t. _ et al_. @xcite also worked on developing an ann model to    forecast electricity market prices with a special feature of dynamism . 
   this model @xcite performs well when a small set of data is trained ,    however , it is likely to perform poorly with large number of data due    to the computational complexities involved . 
   load data from year 2006    to 2009 were gathered and used to develop an ann model for short - term    load forecast in @xcite . in @xcite , ann hybrid with invasive weed    optimization ( iwo ) 
   was employed to forecast the electricity prices in    the australian market . 
   the hybrid model @xcite showed good    performance , however , the focus was on predicting electricity prices    in australia . 
   most of the ann models developed in existing work    considered some specific geographic area@xcite , some models    were able to forecast energy consumption for buildings ( * ? ? ? 
   * ; * ? ? ? 
   * ;    * ? ? ? 
   * ) and only for few hours@xcite . 
   this study used available historical data from year 1994 to 2013 ( but trained data from year 1994 to 2011 ) in determining a suitable model . 
   the resulting model from training will be used to predict electric energy consumption for future years ] , while the error criteria such as mean squared error ( mse ) , root mean squared error ( rmse ) , mean absolute error ( mae ) and mean absolute percentage error ( mape ) are used as measures to justify the appropriate model @xcite . 
   the model was used to predict the behavior for year 2012 and 2013 . 
   the remainder of this paper is organized as follows : section [ sec:2 ] gives a brief description of the ann concept . 
   section [ sec:3 ] presents the ann approach used to analyse our data . 
   section [ sec:4 ] evaluates the performance of the ann model . 
   section [ sec:5 ] draws conclusions . 
   ann is a system based on the working principles of biological neural networks , and is defined as a mimicry of biological neural systems . 
   ann s are at the vanguard of computational systems designed to create or mimic intelligent behavior . unlike classical artificial intelligence ( ai ) systems that are aimed at directly emulating rational and logical reasoning , 
   ann s are targeted at reproducing the causal processing mechanisms that give results to intelligence as a developing property of complex systems . 
   ann systems have been designed for fields such as capacity planning , business intelligence , pattern recognition , robotics . . 
   in computer science and engineering , ann techniques has gained a lot of grounds and is vastly deployed in areas such as forecasting , data analytics and even data mining . 
   the science of raw data examination with the aim of deriving useful conclusions can simply be defined as data analytics . 
   on the other hand , data mining describes the process of determining new patterns from large data sets , by applying a vast set of approaches from statistics , artificial intelligence , or database management . 
   forecasting is useful in predicting future trends with reliance on past data . 
   the focus on this paper will be on using the ann approach in forecasting energy consumption . 
   practically , ann provides accurate approximation of both linear and non - linear functions .        a mathematical abstraction of the internal structure of a neuron is shown in fig . 
   [ fig:1 ] . despite the presence of noise or incomplete information 
   , it is possible for neurons to learn the behaviour or trends and consequently , make useful conclusions . 
   usually , a neural network is trained to perform a specific function by adjusting weight values between elements as seen in fig . [ fig:2](a ) . 
   the neural network function is mainly determined by the connections between the elements @xcite . by observing the data , 
   it is possible for the ann to make accurate predictions . 
   unlike other forecasting approach , the ann technique has the ability to predict future trends with theoretically poor , but rich data set . 
   in this section , we present the overall modeling process . 
   the implementation of the ann model can be described using the flow chart in fig . [ fig:2](b ) . 
   historical monthly data ( historical energy consumption , mean atmospheric temperature , mean relative humidity , population , gdp per capita ) has been gathered from years 1994 to 2013 from gaza region as shown in fig . 
   [ fig:3 ] .        based on the gathered data , 
   this study will develop a forecast model that predicts energy consumption for year 2012 and 2013 . using the ann technique , training and learning procedures are fundamental in forecasting future events . 
   the training of feed - forward networks is usually carried out in a supervised manner @xcite . with a set of data to be trained ( usually extracted from the historical data ) , it is possible to derive an efficient forecast model . 
   the proper selection of inputs for ann training plays a vital role to the success of the training process . on the other hand , 
   the learning process involves providing both input and output data , the network processes the input and compares the resulting output with desired result . 
   the system then adjusts the weights which acts as a control for error minimization .        in order to minimize error 
   , the process is repeated until a satisfactory criterion for convergence is attained . 
   the knowledge acquired by the ann via the learning process is tested by applying it to a new data set that has not been used before , called the testing set . 
   it should now be possible that the network is able to make generalizations and provide accurate result for new data . 
   due to insufficient information , some networks do not converge . 
   it is also noteworthy that over - training the ann can seriously deteriorate forecasts . 
   also , if the ann is fed with redundant or inaccurate information , it may destabilize the system . 
   training and learning process should be thorough in order to achieve good results . to accurately forecast 
   , it is imperative to consider all possible factors that influence electricity energy consumption , which is not feasible in reality . 
   electric energy consumption is influenced by a number of factors , which includes : historical energy consumption , mean atmospheric temperature , mean relative humidity , population , gdp per capita , ppp , etc . in this paper , 
   different criteria were used to evaluate the accuracy of the ann approach in forecasting electric energy consumption in gaza . 
   they include : mean squared error ( mse ) , root mean squared error ( rmse ) , mean absolute error ( mae ) and mean absolute percentage error ( mape ) . 
   a popular and important criterion used for performance analysis is the mse . 
   it is used to relay concepts of bias , precision and accuracy in statistical estimation . here 
   , the difference between the estimated and the actual value is used to get the error , the average of the square of the error gives an expression for mse . 
   the mse criterion is expressed in equation ( [ eqn:1 ] ) . 
   @xmath0 where  @xmath1 is the actual data and  @xmath2 is the forecasted data . 
   the rmse is a quadratic scoring rule which measures the average magnitude of the error . 
   the rmse criterion is expressed in equation ( [ eqn:2 ] ) . 
   rmse usually provides a relatively high weight to large errors due to the fact that averaging is carried out after errors are squared . 
   this makes this criterion an important tool when large errors are specifically undesired . @xmath3 
   the mae measures the average error function for forecasts and actual data with polarity elimination . 
   equation ( [ eqn:3 ] ) gives the expression for the mae criterion used . 
   the mae is a linear score which implies that all the individual differences are weighted equally in the average . 
   @xmath4 mape , on the other hand , measures the size of the error in percentage ( % ) terms . 
   it is calculated as the average of the unsigned percentage error . 
   equation ( [ eqn:4 ] ) gives the expression for the mape criterion used . 
   @xmath5 validation techniques are employed to tackle fundamental problems in pattern recognition ( model selection and performance estimation ) . in this study , 2-fold and 
   k - fold cross validation techniques will be employed and the validation set will only be used as part of training and not part of the test set . 
   the test set will be used to evaluate how well the learning algorithm works as a whole . 
   the forecast model was simulated to obtain results of the energy consumed for year 2012 and 2013 in gaza . 
   table 1 compares the actual and the forecasted energy consumption for year 2012 . 
   2-fold and k - fold cross validation techniques were used and the performance of the forecast model based on different error criteria is shown in table 2 . 
   similarly , table 3 and 4 shows the results for year 2013 . 
   the results obtained have good accuracy and shows that the proposed ann model can be used to predict future trends of electric energy consumption in gaza . 
   .2-fold and k - fold cross validation for year 2012 [ cols="^,^,^,^",options="header " , ]     [ tab:4 ] 
   in this paper , an ann model to forecast electric energy consumption in the gaza strip was presented . to the best of our knowledge , 
   this is the first of it s kind in existing literature . 
   based on the performance evaluation , the error criteria were within tolerable bounds . 
   empirical results presented in this paper indicates the relevance of the proposed ann approach in forecasting electric energy consumption . 
   future works will consider other forecasting techniques . 
   park d. c. , el - sharkawi m. a. , marks ii r. j. , atlas l. e. and damborg m. j.,(1991 ) electric load forecasting using an artificial neural network , _ ieee transactions on power engineering _ , vol.6 , pp . 
   442 - 449    zhou r. , pan y. , huand z. and wang q. , ( 2013 ) building energy use prediction using time series analysis , _ 
   ieee 6th international conference on service - oriented computing and applications _ , pp . 
   309 - 313 . 
   basu k. , debusschere v. and bacha s. , ( 2012 ) appliance usage prediction using a time series based classification approach , _ 
   ieee 38th annual conference on ieee industrial electronics society _ , pp . 
   1217 - 1222 .                      catalo j. p. s. , mariano s. j. p. s. , mendes v. m. f. and ferreira l. a. f. m. , ( 2007 ) an artificial neural network approach for short - term electricity prices forecasting , _ ieee intelligent systems applications to power systems _ , pp 
   . 1 - 7 . 
   pinto t. , sousa t. m. and vale z. , ( 2012 ) dynamic artificial neural network for electricity market prices forecast , _ 
   ieee 16th international conference on intelligent engineering systems _ , pp . 
   311 - 316 .    khamis m. f. i. , baharudin z. , hamid n. h. , abdullah m. f. and solahuddin s. , ( 2011 ) electricity forecasting for small scale power system using artificial neural network , _ 
   ieee 5th international power engineering and optimization conference _ , pp . 
   54 - 59 . 
   safari m. , dahlan y. n. , razali n. s. and rahman t. k. , ( 2013 ) electricity prices forecasting using ann hybrid with invasive weed optimization ( iwo ) , _ ieee 3rd international conference on system engineering and technology _ , pp . 
   275 - 280 .
summary: due to imprecision and uncertainties in predicting real world problems , artificial neural network ( ann ) techniques have become increasingly useful for modeling and optimization . 
   this paper presents an artificial neural network approach for forecasting electric energy consumption . for effective planning and operation of power systems , 
   optimal forecasting tools are needed for energy operators to maximize profit and also to provide maximum satisfaction to energy consumers . 
   monthly data for electric energy consumed in the gaza strip was collected from year 1994 to 2013 . 
   data was trained and the proposed model was validated using 2-fold and k - fold cross validation techniques . 
   the model has been tested with actual energy consumption data and yields satisfactory performance . 
   example.eps gsave newpath 20 20 moveto 20 220 lineto 220 220 lineto 220 20 lineto closepath 2 setlinewidth gsave .4 setgray fill grestore stroke grestore